diff --git a/dsLightRag/Start.py b/dsLightRag/Start.py
index 6ce31f51..f3651ae3 100644
--- a/dsLightRag/Start.py
+++ b/dsLightRag/Start.py
@@ -36,17 +36,18 @@ app = FastAPI(lifespan=lifespan)
 # 挂载静态文件目录
 app.mount("/static", StaticFiles(directory="Static"), name="static")
 
+
 # 访问根的跳转
 @app.get("/")
 async def redirect_to_ai():
     return fastapi.responses.RedirectResponse(url="/static/ai.html")
 
+
 @app.post("/api/rag")
 async def rag(request: fastapi.Request):
     data = await request.json()
-    logger.info(f"Received request: {data}")
     workspace = data.get("topic", "ShiJi")  # Chinese, Math ,ShiJi 默认是少年读史记
-    mode = data.get("mode", "hybrid")  # 默认为hybrid模式
+    logger.info("工作空间:" + workspace)
     # 查询的问题
     query = data.get("query")
 
@@ -108,9 +109,7 @@ async def rag(request: fastapi.Request):
             rag = await initialize_pg_rag(WORKING_DIR=WORKING_DIR, workspace=workspace)
             resp = await rag.aquery(
                 query=query,
-                param=QueryParam(mode=mode, stream=True, user_prompt=user_prompt))
-            # hybrid naive
-
+                param=QueryParam(mode="hybrid", stream=True, user_prompt=user_prompt))
             async for chunk in resp:
                 if not chunk:
                     continue
@@ -184,15 +183,15 @@ async def get_tree_data():
         async with pg_pool.acquire() as conn:
             # 执行查询
             rows = await conn.fetch("""
-                                  SELECT id,
-                                         title,
-                                         parent_id,
-                                         is_leaf,
-                                         prerequisite,
-                                         related
-                                  FROM knowledge_points
-                                  ORDER BY parent_id, id
-                                  """)
+                                    SELECT id,
+                                           title,
+                                           parent_id,
+                                           is_leaf,
+                                           prerequisite,
+                                           related
+                                    FROM knowledge_points
+                                    ORDER BY parent_id, id
+                                    """)
         # 构建节点映射
         nodes = {}
         for row in rows:
@@ -249,10 +248,10 @@ async def update_knowledge(request: fastapi.Request):
         async with pg_pool.acquire() as conn:
             if update_type == 'prerequisite':
                 await conn.execute("""
-                    UPDATE knowledge_points 
-                    SET prerequisite = $1 
-                    WHERE id = $2
-                """,
+                                   UPDATE knowledge_points
+                                   SET prerequisite = $1
+                                   WHERE id = $2
+                                   """,
                                    json.dumps(
                                        [{"id": p["id"], "title": p["title"]} for p in knowledge],
                                        ensure_ascii=False
@@ -260,10 +259,10 @@ async def update_knowledge(request: fastapi.Request):
                                    node_id)
             else:  # related knowledge
                 await conn.execute("""
-                    UPDATE knowledge_points 
-                    SET related = $1 
-                    WHERE id = $2
-                """,
+                                   UPDATE knowledge_points
+                                   SET related = $1
+                                   WHERE id = $2
+                                   """,
                                    json.dumps(
                                        [{"id": p["id"], "title": p["title"]} for p in knowledge],
                                        ensure_ascii=False
@@ -310,10 +309,11 @@ async def get_sources(page: int = 1, limit: int = 10):
             offset = (page - 1) * limit
             rows = await conn.fetch(
                 """
-                SELECT id, account_id,account_name, created_at 
-                FROM t_wechat_source 
-                ORDER BY created_at DESC 
-                LIMIT $1 OFFSET $2
+                SELECT id, account_id, account_name, created_at
+                FROM t_wechat_source
+                ORDER BY created_at DESC
+                    LIMIT $1
+                OFFSET $2
                 """,
                 limit, offset
             )
@@ -352,11 +352,16 @@ async def get_articles(page: int = 1, limit: int = 10):
             offset = (page - 1) * limit
             rows = await conn.fetch(
                 """
-                SELECT a.id, a.title, a.source as name, 
-                       a.publish_time, a.collection_time,a.url
+                SELECT a.id,
+                       a.title,
+                       a.source as name,
+                       a.publish_time,
+                       a.collection_time,
+                       a.url
                 FROM t_wechat_articles a
-                ORDER BY a.collection_time DESC 
-                LIMIT $1 OFFSET $2
+                ORDER BY a.collection_time DESC
+                    LIMIT $1
+                OFFSET $2
                 """,
                 limit, offset
             )
diff --git a/dsLightRag/Util/LightRagUtil.py b/dsLightRag/Util/LightRagUtil.py
index 9e42b8d7..b1f78cd2 100644
--- a/dsLightRag/Util/LightRagUtil.py
+++ b/dsLightRag/Util/LightRagUtil.py
@@ -152,14 +152,14 @@ os.environ["POSTGRES_PASSWORD"] = POSTGRES_PASSWORD
 os.environ["POSTGRES_DATABASE"] = POSTGRES_DATABASE
 
 
-async def initialize_pg_rag(WORKING_DIR, workspace='default'):
+async def initialize_pg_rag(WORKING_DIR, workspace):
     rag = LightRAG(
         working_dir=WORKING_DIR,
         llm_model_func=llm_model_func,
         llm_model_name=LLM_MODEL_NAME,
         llm_model_max_async=4,
         llm_model_max_token_size=32768,
-        enable_llm_cache_for_entity_extract=True,
+        enable_llm_cache_for_entity_extract=False,  # 这里黄海修改了一下，不知道是不是有用
         embedding_func=EmbeddingFunc(
             embedding_dim=EMBED_DIM,
             max_token_size=EMBED_MAX_TOKEN_SIZE,