import hashlib import os import time from pathlib import Path from langchain_text_splitters import MarkdownTextSplitter from src import config from src.utils import hashstr, logger from src.utils.datetime_utils import utc_isoformat def validate_file_path(file_path: str, db_id: str = None) -> str: """ 验证文件路径安全性，防止路径遍历攻击 Args: file_path: 要验证的文件路径 db_id: 数据库ID，用于获取知识库特定的上传目录 Returns: str: 规范化后的安全路径 Raises: ValueError: 如果路径不安全 """ try: # 规范化路径 normalized_path = os.path.abspath(os.path.realpath(file_path)) # 获取允许的根目录 from src.knowledge import knowledge_base allowed_dirs = [ os.path.abspath(os.path.realpath(config.save_dir)), ] # 如果指定了db_id，添加知识库特定的上传目录 if db_id: try: allowed_dirs.append(os.path.abspath(os.path.realpath(knowledge_base.get_db_upload_path(db_id)))) except Exception: # 如果无法获取db路径，使用通用上传目录 allowed_dirs.append( os.path.abspath(os.path.realpath(os.path.join(config.save_dir, "database", "uploads"))) ) # 检查路径是否在允许的目录内 is_safe = False for allowed_dir in allowed_dirs: try: if normalized_path.startswith(allowed_dir): is_safe = True break except Exception: continue if not is_safe: logger.warning(f"Path traversal attempt detected: {file_path} (normalized: {normalized_path})") raise ValueError(f"Access denied: Invalid file path: {file_path}") return normalized_path except Exception as e: logger.error(f"Path validation failed for {file_path}: {e}") raise ValueError(f"Invalid file path: {file_path}") def split_text_into_chunks(text: str, file_id: str, filename: str, params: dict = {}) -> list[dict]: """ 将文本分割成块，使用 LangChain 的 MarkdownTextSplitter 进行智能分割 """ chunks = [] chunk_size = params.get("chunk_size", 1000) chunk_overlap = params.get("chunk_overlap", 200) # 使用 MarkdownTextSplitter 进行智能分割 # MarkdownTextSplitter 会尝试沿着 Markdown 格式的标题进行分割 text_splitter = MarkdownTextSplitter( chunk_size=chunk_size, chunk_overlap=chunk_overlap, ) text_chunks = text_splitter.split_text(text) # 转换为标准格式 for chunk_index, chunk_content in enumerate(text_chunks): if chunk_content.strip(): # 跳过空块 chunks.append( { "id": f"{file_id}_chunk_{chunk_index}", "content": chunk_content.strip(), "file_id": file_id, "filename": filename, "chunk_index": chunk_index, "source": filename, "chunk_id": f"{file_id}_chunk_{chunk_index}", } ) logger.debug(f"Successfully split text into {len(chunks)} chunks using MarkdownTextSplitter") return chunks def calculate_content_hash(data: bytes | bytearray | str | os.PathLike[str] | Path) -> str: """ 计算文件内容的 SHA-256 哈希值。 Args: data: 文件内容的二进制数据或文件路径 Returns: str: 十六进制哈希值 """ sha256 = hashlib.sha256() if isinstance(data, (bytes, bytearray)): sha256.update(data) return sha256.hexdigest() if isinstance(data, (str, os.PathLike, Path)): path = Path(data) with path.open("rb") as file_handle: for chunk in iter(lambda: file_handle.read(8192), b""): sha256.update(chunk) return sha256.hexdigest() raise TypeError(f"Unsupported data type for hashing: {type(data)!r}") def prepare_item_metadata(item: str, content_type: str, db_id: str) -> dict: """ 准备文件或URL的元数据 """ if content_type == "file": file_path = Path(item) file_id = f"file_{hashstr(str(file_path) + str(time.time()), 6)}" file_type = file_path.suffix.lower().replace(".", "") filename = file_path.name item_path = os.path.relpath(file_path, Path.cwd()) content_hash = None try: if file_path.exists(): content_hash = calculate_content_hash(file_path) except Exception as exc: # noqa: BLE001 logger.warning(f"Failed to calculate content hash for {file_path}: {exc}") else: # URL file_id = f"url_{hashstr(item + str(time.time()), 6)}" file_type = "url" filename = f"webpage_{hashstr(item, 6)}.md" item_path = item content_hash = None return { "database_id": db_id, "filename": filename, "path": item_path, "file_type": file_type, "status": "processing", "created_at": utc_isoformat(), "file_id": file_id, "content_hash": content_hash, } def split_text_into_qa_chunks( text: str, file_id: str, filename: str, qa_separator: None | str = None, params: dict = {} ) -> list[dict]: """ 将文本按QA对分割成块，使用 LangChain 的 CharacterTextSplitter 进行分割""" qa_separator = qa_separator or "\n\n" text_chunks = text.split(qa_separator) # 转换为标准格式 chunks = [] for chunk_index, chunk_content in enumerate(text_chunks): if chunk_content.strip(): # 跳过空块 chunk_content = chunk_content.strip()[:4096] chunks.append( { "id": f"{file_id}_qa_chunk_{chunk_index}", "content": chunk_content.strip(), "file_id": file_id, "filename": filename, "chunk_index": chunk_index, "source": filename, "chunk_id": f"{file_id}_qa_chunk_{chunk_index}", "chunk_type": "qa", # 标识为QA类型的chunk } ) logger.debug(f"QA chunks: {chunks[0]}") logger.debug( f"Successfully split QA text into {len(chunks)} chunks using CharacterTextSplitter with `{qa_separator=}`" ) return chunks def get_embedding_config(embed_info: dict) -> dict: """ 获取嵌入模型配置 Args: embed_info: 嵌入信息字典 Returns: dict: 标准化的嵌入配置 """ config_dict = {} try: if embed_info: # 处理 embed_info 可能是字典或 EmbedModelInfo 对象的情况 if hasattr(embed_info, "name"): # EmbedModelInfo 对象 config_dict["model"] = embed_info.name config_dict["api_key"] = os.getenv(embed_info.api_key, embed_info.api_key) config_dict["base_url"] = embed_info.base_url config_dict["dimension"] = embed_info.dimension else: # 字典形式 config_dict["model"] = embed_info["name"] config_dict["api_key"] = os.getenv(embed_info["api_key"], embed_info["api_key"]) config_dict["base_url"] = embed_info["base_url"] config_dict["dimension"] = embed_info.get("dimension", 1024) else: from src.models import select_embedding_model default_model = select_embedding_model(config.embed_model) config_dict["model"] = default_model.model config_dict["api_key"] = default_model.api_key config_dict["base_url"] = default_model.base_url config_dict["dimension"] = getattr(default_model, "dimension", 1024) except Exception as e: logger.error(f"Error in get_embedding_config: {e}, {embed_info}") raise ValueError(f"Error in get_embedding_config: {e}") logger.debug(f"Embedding config: {config_dict}") return config_dict