diff --git a/src/memos/api/handlers/search_handler.py b/src/memos/api/handlers/search_handler.py
index 6e2f6f712..c8b92e225 100644
--- a/src/memos/api/handlers/search_handler.py
+++ b/src/memos/api/handlers/search_handler.py
@@ -191,7 +191,7 @@ def _fast_search(
         """
         target_session_id = search_req.session_id or "default_session"
         search_filter = {"session_id": search_req.session_id} if search_req.session_id else None
-        plugin = bool(search_req.info is not None and search_req.info.get("origin_model"))
+        plugin = bool(search_req.source is not None and search_req.source == "plugin")
         search_results = self.naive_mem_cube.text_mem.search(
             query=search_req.query,
             user_name=user_context.mem_cube_id,
diff --git a/src/memos/api/product_models.py b/src/memos/api/product_models.py
index 85c4d21cd..c238e7d09 100644
--- a/src/memos/api/product_models.py
+++ b/src/memos/api/product_models.py
@@ -185,7 +185,7 @@ class APISearchRequest(BaseRequest):
     )
     include_preference: bool = Field(True, description="Whether to handle preference memory")
     pref_top_k: int = Field(6, description="Number of preference results to return")
-    info: dict | None = Field(None, description="Info for search")
+    source: str | None = Field(None, description="Source of the search")
 
 
 class APIADDRequest(BaseRequest):
diff --git a/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py b/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py
index 02a951a09..56aa92e6e 100644
--- a/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py
+++ b/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py
@@ -275,6 +275,7 @@ def _retrieve_simple(
             user_name=user_name,
             use_fast_graph=self.use_fast_graph,
         )
+        logger.info(f"[SIMPLESEARCH] Items count: {len(items)}")
         documents = [getattr(item, "memory", "") for item in items]
         documents_embeddings = self.embedder.embed(documents)
         similarity_matrix = cosine_similarity_matrix(documents_embeddings)