diff --git a/scripts/rag_build_query.py b/scripts/rag_build_query.py
index a1e793e..d6f241c 100644
--- a/scripts/rag_build_query.py
+++ b/scripts/rag_build_query.py
@@ -1,4 +1,5 @@
 import os
+import logging
 from typing import List
 import faiss
 from llama_index import (
@@ -16,6 +17,10 @@ from scripts.permissions import get_user_allowed_indexes
 USER_INDEX_PATH = "index_data"
 USER_DOC_PATH = "docs"
 
+# 设置日志配置
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
 # ✅ 自动加前缀的 BGE-m3 embedding 封装类
 class BGEEmbedding(HuggingFaceEmbedding):
     def _get_query_embedding(self, query: str) -> List[float]:
@@ -27,47 +32,68 @@ class BGEEmbedding(HuggingFaceEmbedding):
         return super()._get_query_embeddings([prefix + q for q in queries])
 
 def build_user_index(user_id: str):
+    logger.info(f"开始为用户 {user_id} 构建索引...")
     doc_dir = os.path.join(USER_DOC_PATH, user_id)
+    
     if not os.path.exists(doc_dir):
         raise FileNotFoundError(f"文档目录不存在: {doc_dir}")
-
+    
+    logger.info(f"发现文档目录: {doc_dir}")
+    
     documents = SimpleDirectoryReader(doc_dir).load_data()
+    logger.info(f"载入文档数量: {len(documents)}")
+    
     embed_model = BGEEmbedding(model_name=settings.MODEL_NAME)
+    logger.info(f"使用模型: {settings.MODEL_NAME}")
+    
     service_context = ServiceContext.from_defaults(embed_model=embed_model, llm=None)
 
     faiss_index = faiss.IndexFlatL2(1024)
     vector_store = FaissVectorStore(faiss_index=faiss_index)
+
     persist_dir = os.path.join(USER_INDEX_PATH, user_id)
     os.makedirs(persist_dir, exist_ok=True)
-
+    logger.info(f"索引保存路径: {persist_dir}")
+    
     storage_context = StorageContext.from_defaults(
         persist_dir=persist_dir,
         vector_store=vector_store,
     )
 
-    index = VectorStoreIndex.from_documents(
-        documents,
-        service_context=service_context,
-        storage_context=storage_context
-    )
-
-    index.persist(persist_dir=persist_dir)
-    print(f"[BUILD] 为用户 {user_id} 构建并保存了完整索引 → {persist_dir}")
+    try:
+        index = VectorStoreIndex.from_documents(
+            documents,
+            service_context=service_context,
+            storage_context=storage_context
+        )
+        index.persist(persist_dir=persist_dir)
+        logger.info(f"索引已保存到 {persist_dir}")
+    except Exception as e:
+        logger.error(f"索引构建失败: {e}")
+        raise HTTPException(status_code=500, detail="索引构建失败")
 
 def query_user_rag(user_id: str, question: str, top_k: int = 4) -> str:
+    logger.info(f"为用户 {user_id} 查询问题：{question}")
+    
     embed_model = BGEEmbedding(model_name=settings.MODEL_NAME)
     service_context = ServiceContext.from_defaults(embed_model=embed_model, llm=None)
 
     persist_dir = os.path.join(USER_INDEX_PATH, user_id)
     if not os.path.exists(persist_dir):
-        raise FileNotFoundError(f"[ERROR] 用户 {user_id} 的索引目录不存在")
+        raise FileNotFoundError(f"用户 {user_id} 的索引目录不存在")
 
+    logger.info(f"加载索引目录: {persist_dir}")
+    
     storage_context = StorageContext.from_defaults(persist_dir=persist_dir)
     index = VectorStoreIndex.load_from_storage(storage_context, service_context=service_context)
 
+    logger.info(f"加载索引成功，开始检索相关文档...")
+    
     all_nodes = index.as_retriever(similarity_top_k=top_k).retrieve(question)
 
     shared_indexes = get_user_allowed_indexes(user_id)
+    logger.info(f"用户 {user_id} 被允许共享的索引：{shared_indexes}")
+    
     for shared_name in shared_indexes:
         shared_dir = os.path.join(USER_INDEX_PATH, shared_name)
         if os.path.exists(shared_dir) and shared_dir != persist_dir:
@@ -75,6 +101,8 @@ def query_user_rag(user_id: str, question: str, top_k: int = 4) -> str:
             shared_index = VectorStoreIndex.load_from_storage(shared_context, service_context=service_context)
             all_nodes += shared_index.as_retriever(similarity_top_k=top_k).retrieve(question)
 
+    logger.info(f"共检索到 {len(all_nodes)} 个相关文档")
+
     sorted_nodes = sorted(all_nodes, key=lambda n: -(n.score or 0))
     top_nodes = sorted_nodes[:top_k]
 
@@ -84,12 +112,12 @@ def query_user_rag(user_id: str, question: str, top_k: int = 4) -> str:
     )
     final_prompt = prompt_template.format(context=context_str, query=question)
 
-    print("[PROMPT构建完成]")
+    logger.info("[PROMPT构建完成]")
     return final_prompt
 
 if __name__ == "__main__":
     uid = "user_001"
     build_user_index(uid)
     prompt = query_user_rag(uid, "这份资料中提到了哪些关键点？")
-    print("\n------ 最终构建的 Prompt 给 LLM 使用 ------\n")
-    print(prompt)
+    logger.info("\n------ 最终构建的 Prompt 给 LLM 使用 ------\n")
+    logger.info(prompt)