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/vector_store.py

@@ -0,0 +1,98 @@
+"""
+LangChain 1.0 向量存储封装
+"""
+import os
+import json
+import hashlib
+from typing import List, Dict, Any, Optional
+from pathlib import Path
+from langchain_chroma import Chroma
+from langchain_core.documents import Document as LangChainDocument
+
+from ..core.config import get_settings
+from .embeddings import get_embedding_model
+
+settings = get_settings()
+
+
+class VectorStore:
+    """向量存储管理器（LangChain 1.0 Chroma）"""
+    
+    def __init__(self):
+        """初始化向量存储"""
+        self.vector_store_path = Path(settings.vector_store_path)
+        self.vector_store_path.mkdir(parents=True, exist_ok=True)
+        
+        # 获取嵌入模型（保留自定义实现）
+        self.embedding_model = get_embedding_model()
+        
+        # 使用LangChain Chroma wrapper
+        self.vectorstore = Chroma(
+            collection_name="course_knowledge",
+            embedding_function=self.embedding_model,
+            persist_directory=str(self.vector_store_path),
+            collection_metadata={"hnsw:space": "l2"}  # 保持L2距离度量
+        )
+    
+    def add_documents(self, documents: List[LangChainDocument]) -> bool:
+        """添加LangChain文档到向量存储"""
+        try:
+            self.vectorstore.add_documents(documents)
+            return True
+        except Exception as e:
+            print(f"添加文档失败: {str(e)}")
+            return False
+    
+    def as_retriever(self, **kwargs):
+        """返回标准LangChain检索器"""
+        return self.vectorstore.as_retriever(**kwargs)
+    
+    def similarity_search_with_score(
+        self, 
+        query: str, 
+        k: int = 5, 
+        filter: Optional[Dict] = None
+    ):
+        """相似度搜索（带分数）"""
+        print(f"[DEBUG-VectorStore] 查询参数 - k: {k}, filter: {filter}")
+        
+        result = self.vectorstore.similarity_search_with_score(
+            query=query,
+            k=k,
+            filter=filter
+        )
+        
+        print(f"[DEBUG-VectorStore] 返回结果数量: {len(result)}")
+        return result
+    
+    def max_marginal_relevance_search(
+        self,
+        query: str,
+        k: int = 5,
+        fetch_k: int = 20,
+        filter: Optional[Dict] = None
+    ):
+        """MMR搜索（多样性检索）"""
+        return self.vectorstore.max_marginal_relevance_search(
+            query=query,
+            k=k,
+            fetch_k=fetch_k,
+            filter=filter
+        )
+
+
+# 单例模式
+_vector_store_instance = None
+
+def get_vector_store() -> VectorStore:
+    """获取向量存储实例"""
+    global _vector_store_instance
+    if _vector_store_instance is None:
+        _vector_store_instance = VectorStore()
+    return _vector_store_instance
+
+
+
+
+
+