654 lines
21 KiB
Python
654 lines
21 KiB
Python
import asyncio
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import base64
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import os
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import re
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import time
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from pathlib import Path
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import aiofiles
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from docling.datamodel.base_models import InputFormat
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from docling.document_converter import DocumentConverter
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from langchain_community.document_loaders import (
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CSVLoader,
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JSONLoader,
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PyPDFLoader,
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TextLoader,
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UnstructuredHTMLLoader,
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UnstructuredMarkdownLoader,
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UnstructuredWordDocumentLoader,
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)
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from markdownify import markdownify as md_convert
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from yuxi.plugins.parser.zip_utils import process_zip_file as _process_zip_file
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from yuxi.storage.minio import get_minio_client
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from yuxi.utils import logger
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SUPPORTED_FILE_EXTENSIONS: tuple[str, ...] = (
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".txt",
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".md",
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".docx",
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".html",
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".htm",
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".json",
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".csv",
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".xls",
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".xlsx",
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".pdf",
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".pptx",
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".jpg",
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".jpeg",
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".png",
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".bmp",
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".tiff",
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".tif",
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".zip",
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)
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def is_supported_file_extension(file_name: str | os.PathLike[str]) -> bool:
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"""Check whether the given file path has a supported extension."""
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return Path(file_name).suffix.lower() in SUPPORTED_FILE_EXTENSIONS
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# Docling 文档转换器（单例模式）
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_docling_converter: DocumentConverter | None = None
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def _get_docling_converter() -> DocumentConverter:
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"""获取 Docling 文档转换器单例"""
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global _docling_converter
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if _docling_converter is None:
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_docling_converter = DocumentConverter(
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format_options={
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InputFormat.DOCX: None,
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InputFormat.XLSX: None,
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InputFormat.PPTX: None,
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}
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)
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return _docling_converter
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def _resolve_image_storage_params(params: dict | None) -> tuple[str, str]:
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params = params or {}
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image_bucket = params.get("image_bucket") or "public"
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image_prefix = params.get("image_prefix")
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if image_prefix:
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normalized_prefix = str(image_prefix).strip("/")
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if normalized_prefix:
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return image_bucket, normalized_prefix
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db_id = params.get("db_id")
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if db_id:
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return image_bucket, f"{db_id}/kb-images"
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return image_bucket, "unknown/kb-images"
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def _upload_image_to_minio(image_data: bytes, filename: str, bucket_name: str, object_prefix: str) -> str:
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"""上传图片到 MinIO，返回 URL"""
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minio_client = get_minio_client()
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minio_client.ensure_bucket_exists(bucket_name)
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normalized_prefix = object_prefix.strip("/") or "unknown/kb-images"
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timestamp = int(time.time() * 1000000)
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suffix = Path(filename).suffix.lower()
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object_name = f"{normalized_prefix}/{timestamp}_{Path(filename).name}"
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content_type_map = {
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".jpg": "image/jpeg",
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".jpeg": "image/jpeg",
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".png": "image/png",
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".gif": "image/gif",
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".webp": "image/webp",
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".bmp": "image/bmp",
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".tif": "image/tiff",
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".tiff": "image/tiff",
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}
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content_type = content_type_map.get(suffix, "image/jpeg")
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result = minio_client.upload_file(
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bucket_name=bucket_name,
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object_name=object_name,
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data=image_data,
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content_type=content_type,
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)
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return result.url
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def _parse_data_uri(data_uri: str) -> tuple[bytes, str]:
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"""解析 data URI，返回 (image_data, mime_type)"""
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header, base64_data = data_uri.split(",", 1)
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mime_type = header.split(":")[1].split(";")[0]
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image_data = base64.b64decode(base64_data)
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return image_data, mime_type
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def _convert_with_docling(file_path: Path, params: dict | None = None) -> str:
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"""
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使用 Docling 将 docx/xlsx/pptx 转换为 Markdown
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Args:
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file_path: 文件路径
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params: 参数，可包含 image_bucket/image_prefix
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Returns:
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Markdown 字符串
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"""
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params = params or {}
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image_bucket, image_prefix = _resolve_image_storage_params(params)
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converter = _get_docling_converter()
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result = converter.convert(file_path)
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if result.status.name != "SUCCESS":
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raise RuntimeError(f"Docling 转换失败: {result.status}")
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doc = result.document
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# 提取图片并上传到 MinIO
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if hasattr(doc, "pictures") and doc.pictures:
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image_refs: list[tuple[str, bytes]] = []
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for pic in doc.pictures:
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if hasattr(pic, "image") and hasattr(pic.image, "uri"):
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uri = str(pic.image.uri)
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if uri.startswith("data:"):
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image_data, mime_type = _parse_data_uri(uri)
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timestamp = int(time.time() * 1000000) # 微秒级时间戳
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filename = f"image_{timestamp}.{mime_type.split('/')[-1]}"
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image_refs.append((filename, image_data))
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# 上传图片并收集 URL
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image_urls: list[str] = []
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for filename, image_data in image_refs:
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try:
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url = _upload_image_to_minio(image_data, filename, image_bucket, image_prefix)
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image_urls.append(f"")
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except Exception as e:
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logger.error(f"上传图片失败 {filename}: {e}")
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image_urls.append(f"[图片: {filename}]")
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# 导出 Markdown
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markdown = doc.export_to_markdown()
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# 替换 <!-- image --> 占位符为图片 URL
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# Docling 使用 <!-- image --> 作为占位符
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for url in reversed(image_urls):
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markdown = re.sub(r"<!--\s*image\s*-->", url, markdown, count=1)
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return markdown
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# 无图片时直接导出
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return doc.export_to_markdown()
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def _convert_docx_with_python_docx(file_path: Path) -> str:
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"""使用 python-docx 解析 DOCX（Docling 失败时兜底）"""
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from docx import Document
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document = Document(str(file_path))
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blocks: list[str] = []
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for para in document.paragraphs:
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text = para.text.strip()
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if text:
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blocks.append(text)
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for table in document.tables:
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rows: list[list[str]] = []
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for row in table.rows:
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cells = [cell.text.strip().replace("\n", " ") for cell in row.cells]
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if any(cells):
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rows.append(cells)
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if not rows:
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continue
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header = rows[0]
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blocks.append(f"| {' | '.join(header)} |")
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blocks.append(f"| {' | '.join(['---'] * len(header))} |")
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for row in rows[1:]:
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normalized_row = row + [""] * (len(header) - len(row))
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blocks.append(f"| {' | '.join(normalized_row[: len(header)])} |")
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blocks.append("")
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return "\n\n".join(blocks).strip()
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def chunk_with_parser(file_path, params=None):
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"""
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使用文件解析器将文件切分成固定大小的块
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Args:
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file_path: 文件路径
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params: 参数
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"""
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params = params or {}
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chunk_size = int(params.get("chunk_size", 500))
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chunk_overlap = int(params.get("chunk_overlap", 100))
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file_type = Path(file_path).suffix.lower()
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# 选择合适的加载器
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if file_type in [".txt"]:
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loader = TextLoader(file_path)
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elif file_type in [".md"]:
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loader = UnstructuredMarkdownLoader(file_path)
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elif file_type in [".docx", ".doc"]:
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loader = UnstructuredWordDocumentLoader(file_path)
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elif file_type in [".html", ".htm"]:
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loader = UnstructuredHTMLLoader(file_path)
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elif file_type in [".json"]:
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loader = JSONLoader(file_path, jq_schema=".")
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elif file_type in [".csv"]:
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loader = CSVLoader(file_path)
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else:
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raise ValueError(f"不支持的文件类型: {file_type}")
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# 加载文档
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docs = loader.load()
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# 创建文本分割器
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap,
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separators=["\n\n", "\n", ".", " ", ""],
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)
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# 分割文档
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nodes = text_splitter.split_documents(docs)
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# 添加序号信息到metadata
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for i, node in enumerate(nodes):
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if node.metadata is None:
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node.metadata = {}
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node.metadata["chunk_idx"] = i
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return nodes
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def chunk_text(text, params=None):
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"""
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将文本切分成固定大小的块
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"""
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params = params or {}
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chunk_size = int(params.get("chunk_size", 500))
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chunk_overlap = int(params.get("chunk_overlap", 100))
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# 创建文本分割器
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=chunk_size, chunk_overlap=chunk_overlap, separators=["\n\n", "\n", ".", " ", ""]
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)
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# 分割文档
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nodes = text_splitter.split_text(text)
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# 添加序号信息到metadata
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nodes = [{"text": node, "metadata": {"chunk_idx": i}} for i, node in enumerate(nodes)]
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return nodes
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def chunk(text_or_path, params=None):
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raise NotImplementedError("chunk is deprecated, use chunk_with_parser or chunk_text instead")
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def pdfreader(file_path, params=None):
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"""读取PDF文件并返回text文本"""
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if isinstance(file_path, str):
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file_path = Path(file_path)
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assert file_path.exists(), "File not found"
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assert file_path.suffix.lower() == ".pdf", "File format not supported"
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# 使用LangChain的PDF加载器
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loader = PyPDFLoader(str(file_path))
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docs = loader.load()
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# 简单的拼接起来之后返回纯文本
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text = "\n\n".join([d.page_content for d in docs])
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return text
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def plainreader(file_path):
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"""读取普通文本文件并返回text文本"""
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assert os.path.exists(file_path), "File not found"
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# 使用LangChain的文本加载器
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loader = TextLoader(str(file_path))
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docs = loader.load()
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text = "\n\n".join([d.page_content for d in docs])
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return text
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def parse_pdf(file, params=None):
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"""
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解析PDF文件，支持多种OCR方式
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Args:
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file: PDF文件路径
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params: 参数字典，包含enable_ocr设置
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Returns:
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str: 解析得到的文本
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Raises:
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DocumentProcessorException: 处理失败时抛出
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"""
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from yuxi.plugins.parser.base import DocumentProcessorException
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from yuxi.plugins.parser.factory import DocumentProcessorFactory
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params = params or {}
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opt_ocr = params.get("enable_ocr", "disable")
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if opt_ocr == "disable":
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return pdfreader(file, params=params)
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image_bucket, image_prefix = _resolve_image_storage_params(params)
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params.setdefault("image_bucket", image_bucket)
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params.setdefault("image_prefix", image_prefix)
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try:
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return DocumentProcessorFactory.process_file(opt_ocr, file, params)
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except DocumentProcessorException as e:
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logger.error(f"文档处理失败: {e.service_name} - {str(e)}")
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raise
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except Exception as e:
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logger.error(f"PDF 解析失败: {str(e)}")
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raise DocumentProcessorException(f"PDF解析失败: {str(e)}", opt_ocr, "parsing_failed")
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def parse_image(file, params=None):
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"""
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解析图像文件，支持多种OCR方式
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Args:
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file: 图像文件路径
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params: 参数字典，包含enable_ocr设置
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Returns:
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str: 解析得到的文本
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Raises:
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DocumentProcessorException: 处理失败时抛出
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ValueError: 图像文件禁用OCR时抛出
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"""
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from yuxi.plugins.parser.base import DocumentProcessorException
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from yuxi.plugins.parser.factory import DocumentProcessorFactory
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params = params or {}
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opt_ocr = params.get("enable_ocr", "disable")
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# 图像文件必须使用 OCR,不能禁用
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if opt_ocr == "disable":
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raise ValueError(
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"图像文件必须启用OCR才能提取文本内容。"
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"请选择OCR方式 (rapid_ocr/mineru_ocr/mineru_official/pp_structure_v3_ocr) 或移除该文件。"
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)
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image_bucket, image_prefix = _resolve_image_storage_params(params)
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params.setdefault("image_bucket", image_bucket)
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params.setdefault("image_prefix", image_prefix)
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try:
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return DocumentProcessorFactory.process_file(opt_ocr, file, params)
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except DocumentProcessorException as e:
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logger.error(f"图像处理失败: {e.service_name} - {str(e)}")
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raise
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except Exception as e:
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logger.error(f"图像解析失败: {str(e)}")
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raise DocumentProcessorException(f"图像解析失败: {str(e)}", opt_ocr, "parsing_failed")
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async def parse_pdf_async(file, params=None):
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return await asyncio.to_thread(parse_pdf, file, params=params)
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async def parse_image_async(file, params=None):
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return await asyncio.to_thread(parse_image, file, params=params)
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async def process_file_to_markdown(file_path: str, params: dict | None = None) -> str:
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"""
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将不同类型的文件转换为markdown格式 - 支持本地文件和MinIO文件
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Args:
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file_path: 文件路径或MinIO URL
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params: 处理参数，对于ZIP文件需要包含 db_id
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Returns:
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markdown格式内容
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Note:
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对于ZIP文件，会在params中保存处理结果供调用方使用：
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- params['_zip_images_info']: 图片信息列表
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- params['_zip_content_hash']: 内容哈希值
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"""
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import os
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import tempfile
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# 检测是否是MinIO URL
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from yuxi.knowledge.utils.kb_utils import is_minio_url
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if is_minio_url(file_path):
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# 从MinIO下载文件到临时位置
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logger.debug(f"Downloading file from MinIO: {file_path}")
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# 从MinIO URL中提取文件名
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if "?" in file_path:
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file_path_clean = file_path.split("?")[0]
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else:
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file_path_clean = file_path
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original_filename = file_path_clean.split("/")[-1]
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# 创建临时文件
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with tempfile.NamedTemporaryFile(delete=False, suffix=Path(original_filename).suffix) as temp_file:
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temp_path = temp_file.name
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try:
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# 使用通用函数解析MinIO URL并下载文件
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from yuxi.knowledge.utils.kb_utils import parse_minio_url
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from yuxi.storage.minio.client import get_minio_client
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# 解析MinIO URL获取bucket_name和object_name
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bucket_name, object_name = parse_minio_url(file_path)
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# 获取MinIO客户端并下载文件
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minio_client = get_minio_client()
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file_content = await minio_client.adownload_file(bucket_name, object_name)
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# 写入临时文件
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async with aiofiles.open(temp_path, "wb") as f:
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await f.write(file_content)
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logger.debug(f"File downloaded to temp path: {temp_path}")
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# 使用临时文件路径
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actual_file_path = temp_path
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except Exception as e:
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# 清理临时文件
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if os.path.exists(temp_path):
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os.unlink(temp_path)
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logger.error(f"Failed to download file from MinIO: {e}")
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raise ValueError(f"无法从MinIO下载文件: {e}")
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else:
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# 本地文件
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actual_file_path = file_path
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try:
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file_path_obj = Path(actual_file_path)
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file_ext = file_path_obj.suffix.lower()
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original_filename = file_path_obj.name
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if file_ext == ".pdf":
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# 使用 OCR 处理 PDF
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text = await parse_pdf_async(str(file_path_obj), params=params)
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result = f"{text}"
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elif file_ext in [".txt", ".md"]:
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# 直接读取文本文件
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with open(file_path_obj, encoding="utf-8") as f:
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content = f.read()
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result = f"{content}"
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elif file_ext == ".docx":
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# 优先使用 Docling，失败时回退到 python-docx
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try:
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result = _convert_with_docling(file_path_obj, params=params)
|
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except Exception as e:
|
||
logger.warning(f"Docling 解析 DOCX 失败，回退到 python-docx: {file_path_obj.name}, {e}")
|
||
result = _convert_docx_with_python_docx(file_path_obj)
|
||
|
||
elif file_ext == ".pptx":
|
||
# 使用 Docling 处理 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":
|
||
image_bucket, image_prefix = _resolve_image_storage_params(params)
|
||
zip_result = await _process_zip_file(
|
||
str(file_path_obj),
|
||
image_bucket=image_bucket,
|
||
image_prefix=image_prefix,
|
||
)
|
||
|
||
# 将处理结果保存到params中供调用方使用
|
||
if params is not None:
|
||
params["_zip_images_info"] = zip_result["images_info"]
|
||
params["_zip_content_hash"] = zip_result["content_hash"]
|
||
params["_zip_image_bucket"] = image_bucket
|
||
params["_zip_image_prefix"] = image_prefix
|
||
|
||
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_url_to_markdown(url: str, params: dict | None = None) -> str:
|
||
"""
|
||
Fetch a URL and convert its content to Markdown.
|
||
|
||
Args:
|
||
url: The URL to fetch.
|
||
params: Optional parameters (unused, kept for API compatibility).
|
||
|
||
Returns:
|
||
The Markdown content of the URL.
|
||
"""
|
||
logger.info(f"Fetching URL: {url}")
|
||
|
||
try:
|
||
import httpx
|
||
|
||
# 使用异步 HTTP 客户端获取页面
|
||
async with httpx.AsyncClient(timeout=30.0, follow_redirects=True) as client:
|
||
response = await client.get(url, headers={"User-Agent": "Mozilla/5.0"})
|
||
response.raise_for_status()
|
||
html_content = response.text
|
||
|
||
# 使用 readability 提取正文 HTML
|
||
from readability import Document
|
||
|
||
doc = Document(html_content)
|
||
body_html = doc.summary()
|
||
|
||
# 转换为 Markdown
|
||
markdown_content = md_convert(body_html, heading_style="atx")
|
||
|
||
logger.info(f"Successfully converted URL to Markdown: {url}")
|
||
return markdown_content
|
||
|
||
except httpx.HTTPError as e:
|
||
logger.error(f"Failed to fetch URL {url}: {e}")
|
||
raise ValueError(f"Failed to fetch URL: {e}")
|
||
except Exception as e:
|
||
logger.error(f"Failed to process URL {url}: {e}")
|
||
raise ValueError(f"Failed to process URL: {e}")
|