Add kb index status

api/app/vector_store.py

@@ -64,6 +64,8 @@ class VectorStore:
     ):
         """添加文档片段到向量库"""
         collection = self.get_collection(kb_id)
+        if collection is None:
+            raise ValueError(f"Knowledge collection not found for kb_id={kb_id}")
         
         if ids is None:
             ids = [f"chunk-{i}" for i in range(len(documents))]
@@ -93,6 +95,11 @@ class VectorStore:
     ) -> Dict:
         """检索相似文档"""
         collection = self.get_collection(kb_id)
+        if collection is None:
+            raise ValueError(
+                f"Knowledge collection not found for kb_id={kb_id}. "
+                "Please ensure the knowledge base exists and documents are indexed."
+            )
         
         # 生成查询向量
         query_embedding = embedding_service.embed_query(query)
@@ -108,6 +115,8 @@ class VectorStore:
     def get_stats(self, kb_id: str) -> Dict:
         """获取向量库统计"""
         collection = self.get_collection(kb_id)
+        if collection is None:
+            raise ValueError(f"Knowledge collection not found for kb_id={kb_id}")
         return {
             "count": collection.count(),
             "kb_id": kb_id
@@ -116,11 +125,15 @@ class VectorStore:
     def delete_documents(self, kb_id: str, ids: List[str]):
         """删除指定文档片段"""
         collection = self.get_collection(kb_id)
+        if collection is None:
+            return
         collection.delete(ids=ids)
     
     def delete_by_metadata(self, kb_id: str, document_id: str):
         """根据文档 ID 删除所有片段"""
         collection = self.get_collection(kb_id)
+        if collection is None:
+            return
         results = collection.get(where={"document_id": document_id})
         if results["ids"]:
             collection.delete(ids=results["ids"])
@@ -244,9 +257,6 @@ embedding_service = EmbeddingService()
 
 def search_knowledge(kb_id: str, query: str, n_results: int = 5) -> Dict:
     """知识库检索"""
-    # 生成查询向量
-    query_vector = embedding_service.embed_query(query)
-    
     # 检索
     results = vector_store.search(
         kb_id=kb_id,