diff --git a/rag/svr/task_executor.py b/rag/svr/task_executor.py
index 2b3ba46151..3a12f78266 100644
--- a/rag/svr/task_executor.py
+++ b/rag/svr/task_executor.py
@@ -778,6 +778,14 @@ async def run_raptor_for_kb(row, kb_parser_config, chat_mdl, embd_mdl, vector_si
     res = []
     tk_count = 0
     max_errors = int(os.environ.get("RAPTOR_MAX_ERRORS", 3))
+    doc_name_by_id = {}
+    for doc_id in set(doc_ids):
+        ok, source_doc = DocumentService.get_by_id(doc_id)
+        if not ok or not source_doc:
+            continue
+        source_name = getattr(source_doc, "name", "")
+        if source_name:
+            doc_name_by_id[doc_id] = source_name
 
     async def generate(chunks, did):
         nonlocal tk_count, res
@@ -792,11 +800,12 @@ async def run_raptor_for_kb(row, kb_parser_config, chat_mdl, embd_mdl, vector_si
         )
         original_length = len(chunks)
         chunks = await raptor(chunks, kb_parser_config["raptor"]["random_seed"], callback, row["id"])
+        effective_doc_name = row["name"] if did == fake_doc_id else doc_name_by_id.get(did, row["name"])
         doc = {
             "doc_id": did,
             "kb_id": [str(row["kb_id"])],
-            "docnm_kwd": row["name"],
-            "title_tks": rag_tokenizer.tokenize(row["name"]),
+            "docnm_kwd": effective_doc_name,
+            "title_tks": rag_tokenizer.tokenize(effective_doc_name),
             "raptor_kwd": "raptor"
         }
         if row["pagerank"]:
@@ -1047,7 +1056,7 @@ async def do_handle_task(task):
             return
 
         # bind LLM for raptor
-        chat_model_config = get_model_config_by_type_and_name(task_dataset_id, LLMType.CHAT, kb_task_llm_id)
+        chat_model_config = get_model_config_by_type_and_name(task_tenant_id, LLMType.CHAT, kb_task_llm_id)
         chat_model = LLMBundle(task_tenant_id, chat_model_config, lang=task_language)
         # run RAPTOR
         async with kg_limiter: