3d63418adf
重构图谱系统架构，引入适配器模式支持多种图数据库类型。主要变更包括： 1. 新增 GraphAdapter 基类及 LightRAG/Upload 适配器实现 2. 重构前端组件结构，新增 GraphDetailPanel 等可复用组件 3. 实现 useGraph 组合式函数统一管理图谱状态 4. 优化 Neo4j 节点/边数据标准化处理 5. 新增统一图谱 API 接口及对应测试用例 6. 改进前端交互体验，增加节点详情展示等功能 前端组件库重构为模块化结构，提升代码复用性。适配器模式使系统可灵活支持不同图数据源，为后续扩展奠定基础。 Next：上传图谱文件的时候，支持属性解析
389 lines
14 KiB
Python
389 lines
14 KiB
Python
import hashlib
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import os
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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 langchain_text_splitters import MarkdownTextSplitter
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from src import config
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from src.config.static.models import EmbedModelInfo
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from src.utils import hashstr, logger
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from src.utils.datetime_utils import utc_isoformat
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def validate_file_path(file_path: str, db_id: str = None) -> str:
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"""
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验证文件路径安全性，防止路径遍历攻击 - 支持本地文件和MinIO URL
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Args:
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file_path: 要验证的文件路径或MinIO URL
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db_id: 数据库ID，用于获取知识库特定的上传目录
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Returns:
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str: 规范化后的安全路径
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Raises:
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ValueError: 如果路径不安全
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"""
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try:
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# 检测是否是MinIO URL，如果是则直接返回（不进行路径遍历检查）
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if is_minio_url(file_path):
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logger.debug(f"MinIO URL detected, skipping path validation: {file_path}")
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return file_path
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# 规范化路径（仅对本地文件）
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normalized_path = os.path.abspath(os.path.realpath(file_path))
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# 获取允许的根目录
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from src.knowledge import knowledge_base
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allowed_dirs = [
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os.path.abspath(os.path.realpath(config.save_dir)),
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]
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# 如果指定了db_id，添加知识库特定的上传目录
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if db_id:
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try:
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allowed_dirs.append(os.path.abspath(os.path.realpath(knowledge_base.get_db_upload_path(db_id))))
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except Exception:
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# 如果无法获取db路径，使用通用上传目录
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allowed_dirs.append(
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os.path.abspath(os.path.realpath(os.path.join(config.save_dir, "database", "uploads")))
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)
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# 检查路径是否在允许的目录内
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is_safe = False
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for allowed_dir in allowed_dirs:
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try:
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if normalized_path.startswith(allowed_dir):
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is_safe = True
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break
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except Exception:
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continue
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if not is_safe:
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logger.warning(f"Path traversal attempt detected: {file_path} (normalized: {normalized_path})")
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raise ValueError(f"Access denied: Invalid file path: {file_path}")
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return normalized_path
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except Exception as e:
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logger.error(f"Path validation failed for {file_path}: {e}")
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raise ValueError(f"Invalid file path: {file_path}")
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def split_text_into_chunks(text: str, file_id: str, filename: str, params: dict = {}) -> list[dict]:
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"""
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将文本分割成块，使用 LangChain 的 MarkdownTextSplitter 进行智能分割
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"""
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chunks = []
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chunk_size = params.get("chunk_size", 1000)
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chunk_overlap = params.get("chunk_overlap", 200)
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# 使用 MarkdownTextSplitter 进行智能分割
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# MarkdownTextSplitter 会尝试沿着 Markdown 格式的标题进行分割
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text_splitter = MarkdownTextSplitter(
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap,
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)
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text_chunks = text_splitter.split_text(text)
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# 转换为标准格式
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for chunk_index, chunk_content in enumerate(text_chunks):
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if chunk_content.strip(): # 跳过空块
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chunks.append(
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{
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"id": f"{file_id}_chunk_{chunk_index}",
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"content": chunk_content, # .strip(),
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"file_id": file_id,
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"filename": filename,
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"chunk_index": chunk_index,
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"source": filename,
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"chunk_id": f"{file_id}_chunk_{chunk_index}",
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}
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)
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logger.debug(f"Successfully split text into {len(chunks)} chunks using MarkdownTextSplitter")
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return chunks
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async def calculate_content_hash(data: bytes | bytearray | str | os.PathLike[str] | Path) -> str:
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"""
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计算文件内容的 SHA-256 哈希值。
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Args:
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data: 文件内容的二进制数据或文件路径
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Returns:
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str: 十六进制哈希值
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"""
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sha256 = hashlib.sha256()
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if isinstance(data, (bytes, bytearray)):
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sha256.update(data)
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return sha256.hexdigest()
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if isinstance(data, (str, os.PathLike, Path)):
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path = Path(data)
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async with aiofiles.open(path, "rb") as file_handle:
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chunk = await file_handle.read(8192)
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while chunk:
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sha256.update(chunk)
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chunk = await file_handle.read(8192)
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return sha256.hexdigest()
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raise TypeError(f"Unsupported data type for hashing: {type(data)!r}")
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async def prepare_item_metadata(item: str, content_type: str, db_id: str, params: dict | None = None) -> dict:
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"""
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准备文件或URL的元数据 - 支持本地文件和MinIO文件
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Args:
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item: 文件路径或MinIO URL
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content_type: 内容类型 ("file" 或 "url")
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db_id: 数据库ID
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params: 处理参数，可选
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"""
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if content_type == "file":
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# 检测是否是MinIO URL还是本地文件路径
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if is_minio_url(item):
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# MinIO文件处理
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logger.debug(f"Processing MinIO file: {item}")
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# 从MinIO URL中提取文件名
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if "?" in item:
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# URL可能包含查询参数，去掉它们
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item_clean = item.split("?")[0]
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else:
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item_clean = item
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# 获取文件名（从路径的最后部分）
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filename = item_clean.split("/")[-1]
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# 如果文件名包含时间戳，提取原始文件名
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import re
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timestamp_pattern = r"^(.+)_(\d{13})(\.[^.]+)$"
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match = re.match(timestamp_pattern, filename)
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if match:
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original_filename = match.group(1) + match.group(3)
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# 存储原始文件名用于显示
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filename_display = original_filename
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else:
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filename_display = filename
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file_type = filename.split(".")[-1].lower() if "." in filename else ""
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item_path = item # 保持MinIO URL作为路径
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# 从URL或params中获取content_hash（如果有的话）
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content_hash = None
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if params and "content_hash" in params:
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content_hash = params["content_hash"]
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else:
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# 本地文件处理
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file_path = Path(item)
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file_type = file_path.suffix.lower().replace(".", "")
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filename = file_path.name
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filename_display = filename
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item_path = os.path.relpath(file_path, Path.cwd())
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content_hash = None
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try:
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if file_path.exists():
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content_hash = await calculate_content_hash(file_path)
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except Exception as exc: # noqa: BLE001
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logger.warning(f"Failed to calculate content hash for {file_path}: {exc}")
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# 生成文件ID
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file_id = f"file_{hashstr(str(item_path) + str(time.time()), 6)}"
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else:
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raise ValueError("URL 元数据生成已禁用")
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metadata = {
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"database_id": db_id,
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"filename": filename_display, # 使用显示用的文件名
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"path": item_path,
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"file_type": file_type,
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"status": "processing",
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"created_at": utc_isoformat(),
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"file_id": file_id,
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"content_hash": content_hash,
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}
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# 保存处理参数到元数据
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if params:
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metadata["processing_params"] = params.copy()
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return metadata
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def split_text_into_qa_chunks(
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text: str, file_id: str, filename: str, qa_separator: None | str = None, params: dict = {}
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) -> list[dict]:
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"""
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将文本按QA对分割成块，使用 LangChain 的 CharacterTextSplitter 进行分割"""
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qa_separator = qa_separator or "\n\n"
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text_chunks = text.split(qa_separator)
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# 转换为标准格式
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chunks = []
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for chunk_index, chunk_content in enumerate(text_chunks):
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if chunk_content.strip(): # 跳过空块
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chunk_content = chunk_content.strip()[:4096]
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chunks.append(
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{
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"id": f"{file_id}_qa_chunk_{chunk_index}",
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"content": chunk_content.strip(),
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"file_id": file_id,
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"filename": filename,
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"chunk_index": chunk_index,
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"source": filename,
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"chunk_id": f"{file_id}_qa_chunk_{chunk_index}",
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"chunk_type": "qa", # 标识为QA类型的chunk
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}
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)
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logger.debug(f"QA chunks: {chunks[0]}")
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logger.debug(
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f"Successfully split QA text into {len(chunks)} chunks using CharacterTextSplitter with `{qa_separator=}`"
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)
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return chunks
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def merge_processing_params(metadata_params: dict | None, request_params: dict | None) -> dict:
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"""
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合并处理参数：优先使用请求参数，缺失时使用元数据中的参数
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Args:
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metadata_params: 元数据中保存的参数
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request_params: 请求中提供的参数
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Returns:
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dict: 合并后的参数
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"""
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merged_params = {}
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# 首先使用元数据中的参数作为默认值
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if metadata_params:
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merged_params.update(metadata_params)
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# 然后使用请求参数覆盖（如果提供）
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if request_params:
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merged_params.update(request_params)
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logger.debug(f"Merged processing params: {metadata_params=}, {request_params=}, {merged_params=}")
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return merged_params
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def get_embedding_config(embed_info: dict) -> dict:
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"""
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获取嵌入模型配置
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Args:
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embed_info: 嵌入信息字典
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Returns:
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dict: 标准化的嵌入配置
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"""
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try:
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# 检查 embed_info 是否有效
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if not embed_info or ("model" not in embed_info and "name" not in embed_info):
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logger.error(f"Invalid embed_info: {embed_info}, using default embedding model config")
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raise ValueError("Invalid embed_info: must be a non-empty dictionary")
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# 优先检查是否有 model_id 字段
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if "model_id" in embed_info and embed_info["model_id"]:
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logger.warning(f"Using model_id: {embed_info['model_id']}")
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config_dict = config.embed_model_names[embed_info["model_id"]].model_dump()
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config_dict["api_key"] = os.getenv(config_dict["api_key"]) or config_dict["api_key"]
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return config_dict
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# 检查是否是 EmbedModelInfo 对象（在某些情况下可能直接传入对象）
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if hasattr(embed_info, "name") and isinstance(embed_info, EmbedModelInfo):
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logger.debug(f"Using EmbedModelInfo object: {embed_info.name}")
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config_dict = embed_info.model_dump()
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config_dict["api_key"] = os.getenv(config_dict["api_key"]) or config_dict["api_key"]
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return config_dict
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# 字典形式（保持向后兼容）
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# 检查必需字段是否存在
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if not embed_info.get("name") or not embed_info.get("base_url"):
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logger.warning(f"embed_info missing required 'name' or 'base_url' field: {embed_info}, using default")
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raise ValueError("embed_info missing required 'name' or 'base_url' field")
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config_dict = {
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"model": embed_info["name"],
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"api_key": os.getenv(embed_info["api_key"]) or embed_info["api_key"],
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"base_url": embed_info["base_url"],
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"dimension": embed_info.get("dimension", 1024),
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}
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logger.debug(f"Embedding config from dict: {config_dict}")
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return config_dict
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except Exception as e:
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logger.error(f"Error in get_embedding_config: {e}, embed_info={embed_info}")
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# 返回默认配置作为fallback
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logger.warning("Falling back to default embedding model config")
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try:
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config_dict = config.embed_model_names[config.embed_model].model_dump()
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config_dict["api_key"] = os.getenv(config_dict["api_key"]) or config_dict["api_key"]
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return config_dict
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except Exception as fallback_error:
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logger.error(f"Failed to get default embedding config: {fallback_error}")
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raise ValueError(f"Failed to get embedding config and fallback failed: {e}")
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def is_minio_url(file_path: str) -> bool:
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"""
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检测是否是MinIO URL
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Args:
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file_path: 文件路径或URL
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Returns:
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bool: 是否是MinIO URL
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"""
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return file_path.startswith(("http://", "https://", "s3://")) or "minio" in file_path.lower()
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def parse_minio_url(file_path: str) -> tuple[str, str]:
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"""
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解析MinIO URL，提取bucket名称和对象名称
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Args:
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file_path: MinIO文件URL
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Returns:
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tuple[str, str]: (bucket_name, object_name)
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Raises:
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ValueError: 如果无法解析URL
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"""
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try:
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from urllib.parse import urlparse
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# 解析URL
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parsed_url = urlparse(file_path)
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# 从URL路径中提取对象名称（去掉开头的斜杠）
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object_name = parsed_url.path.lstrip("/")
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# 分离bucket名称和对象名称
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path_parts = object_name.split("/", 1)
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if len(path_parts) > 1:
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bucket_name = path_parts[0]
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object_name = path_parts[1]
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else:
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raise ValueError(f"无法解析MinIO URL中的bucket名称: {file_path}")
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logger.debug(f"Parsed MinIO URL: bucket_name={bucket_name}, object_name={object_name}")
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return bucket_name, object_name
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except Exception as e:
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logger.error(f"Failed to parse MinIO URL {file_path}: {e}")
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raise ValueError(f"无法解析MinIO URL: {file_path}")
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