Initial commit: 国土空间规划课程智能体 v1.0

单容器 Docker 架构的国土空间规划课程智能问答系统，集成 FastAPI 后端与 Next.js 前端。

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

backend/src/rag/document_loaders.py

@@ -0,0 +1,43 @@
+"""
+LangChain 1.0 文档加载器封装
+"""
+from typing import List, Optional
+from pathlib import Path
+from langchain_community.document_loaders import (
+    PyPDFLoader,
+    Docx2txtLoader,
+    TextLoader,
+    UnstructuredMarkdownLoader
+)
+from langchain_core.documents import Document
+
+class DocumentLoaderFactory:
+    """文档加载器工厂"""
+    
+    @staticmethod
+    def get_loader(file_path: str, file_type: str):
+        """根据文件类型获取对应的加载器"""
+        loaders = {
+            ".pdf": PyPDFLoader,
+            ".docx": Docx2txtLoader,
+            ".txt": TextLoader,
+            ".md": UnstructuredMarkdownLoader,
+        }
+        
+        loader_class = loaders.get(file_type)
+        if not loader_class:
+            raise ValueError(f"Unsupported file type: {file_type}")
+        
+        return loader_class(file_path)
+    
+    @staticmethod
+    def load_document(file_path: str, file_type: str, metadata: Optional[dict] = None) -> List[Document]:
+        """加载文档并添加元数据"""
+        loader = DocumentLoaderFactory.get_loader(file_path, file_type)
+        documents = loader.load()
+        
+        if metadata:
+            for doc in documents:
+                doc.metadata.update(metadata)
+        
+        return documents