diff --git a/api/utils/api_utils.py b/api/utils/api_utils.py
index 7237a3ce0..db81f2257 100644
--- a/api/utils/api_utils.py
+++ b/api/utils/api_utils.py
@@ -676,12 +676,14 @@ async def is_strong_enough(chat_model, embedding_model):
     @timeout(30, 2)
     async def _is_strong_enough():
         nonlocal chat_model, embedding_model
-        with trio.fail_after(3):
-            _ = await trio.to_thread.run_sync(lambda: embedding_model.encode(["Are you strong enough!?"]))
-        with trio.fail_after(30):
-            res =  await trio.to_thread.run_sync(lambda: chat_model.chat("Nothing special.", [{"role":"user", "content": "Are you strong enough!?"}], {}))
-        if res.find("**ERROR**") >= 0:
-            raise Exception(res)
+        if embedding_model:
+            with trio.fail_after(3):
+                _ = await trio.to_thread.run_sync(lambda: embedding_model.encode(["Are you strong enough!?"]))
+        if chat_model:
+            with trio.fail_after(30):
+                res =  await trio.to_thread.run_sync(lambda: chat_model.chat("Nothing special.", [{"role":"user", "content": "Are you strong enough!?"}], {}))
+            if res.find("**ERROR**") >= 0:
+                raise Exception(res)
 
     # Pressure test for GraphRAG task
     async with trio.open_nursery() as nursery:
diff --git a/graphrag/general/index.py b/graphrag/general/index.py
index 42230f537..ca88a292b 100644
--- a/graphrag/general/index.py
+++ b/graphrag/general/index.py
@@ -20,7 +20,7 @@ import trio
 
 from api import settings
 from api.utils import get_uuid
-from api.utils.api_utils import timeout, is_strong_enough
+from api.utils.api_utils import timeout
 from graphrag.light.graph_extractor import GraphExtractor as LightKGExt
 from graphrag.general.graph_extractor import GraphExtractor as GeneralKGExt
 from graphrag.general.community_reports_extractor import CommunityReportsExtractor
@@ -49,9 +49,6 @@ async def run_graphrag(
     embedding_model,
     callback,
 ):
-    # Pressure test for GraphRAG task
-    await is_strong_enough(chat_model, embedding_model)
-
     start = trio.current_time()
     tenant_id, kb_id, doc_id = row["tenant_id"], str(row["kb_id"]), row["doc_id"]
     chunks = []
diff --git a/rag/svr/task_executor.py b/rag/svr/task_executor.py
index 837a5a8a4..3e76b56bd 100644
--- a/rag/svr/task_executor.py
+++ b/rag/svr/task_executor.py
@@ -184,6 +184,7 @@ def set_progress(task_id, from_page=0, to_page=-1, prog=None, msg="Processing...
     except Exception:
         logging.exception(f"set_progress({task_id}), progress: {prog}, progress_msg: {msg}, got exception")
 
+
 async def collect():
     global CONSUMER_NAME, DONE_TASKS, FAILED_TASKS
     global UNACKED_ITERATOR
@@ -229,6 +230,7 @@ async def get_storage_binary(bucket, name):
     return await trio.to_thread.run_sync(lambda: STORAGE_IMPL.get(bucket, name))
 
 
+@timeout(60*40, 1)
 async def build_chunks(task, progress_callback):
     if task["size"] > DOC_MAXIMUM_SIZE:
         set_progress(task["id"], prog=-1, msg="File size exceeds( <= %dMb )" %
@@ -541,6 +543,7 @@ async def do_handle_task(task):
     try:
         # bind embedding model
         embedding_model = LLMBundle(task_tenant_id, LLMType.EMBEDDING, llm_name=task_embedding_id, lang=task_language)
+        await is_strong_enough(None, embedding_model)
         vts, _ = embedding_model.encode(["ok"])
         vector_size = len(vts[0])
     except Exception as e:
@@ -555,6 +558,7 @@ async def do_handle_task(task):
     if task.get("task_type", "") == "raptor":
         # bind LLM for raptor
         chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language)
+        await is_strong_enough(chat_model, None)
         # run RAPTOR
         async with kg_limiter:
             chunks, token_count = await run_raptor(task, chat_model, embedding_model, vector_size, progress_callback)
@@ -566,6 +570,7 @@ async def do_handle_task(task):
         graphrag_conf = task["kb_parser_config"].get("graphrag", {})
         start_ts = timer()
         chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language)
+        await is_strong_enough(chat_model, None)
         with_resolution = graphrag_conf.get("resolution", False)
         with_community = graphrag_conf.get("community", False)
         async with kg_limiter: