diff --git a/rag/llm/rerank_model.py b/rag/llm/rerank_model.py
index 474b39a81..372ba9e71 100644
--- a/rag/llm/rerank_model.py
+++ b/rag/llm/rerank_model.py
@@ -537,7 +537,7 @@ class HuggingfaceRerank(DefaultRerank):
         return np.array(scores)
 
     def __init__(self, key, model_name="BAAI/bge-reranker-v2-m3", base_url="http://127.0.0.1"):
-        self.model_name = model_name
+        self.model_name = model_name.split("___")[0]
         self.base_url = base_url
 
     def similarity(self, query: str, texts: list) -> tuple[np.ndarray, int]:
diff --git a/rag/svr/task_executor.py b/rag/svr/task_executor.py
index e8ccb5be9..4d0e860be 100644
--- a/rag/svr/task_executor.py
+++ b/rag/svr/task_executor.py
@@ -57,7 +57,7 @@ from rag.app import laws, paper, presentation, manual, qa, table, book, resume,
 from rag.nlp import search, rag_tokenizer
 from rag.raptor import RecursiveAbstractiveProcessing4TreeOrganizedRetrieval as Raptor
 from rag.settings import DOC_MAXIMUM_SIZE, SVR_CONSUMER_GROUP_NAME, get_svr_queue_name, get_svr_queue_names, print_rag_settings, TAG_FLD, PAGERANK_FLD
-from rag.utils import num_tokens_from_string
+from rag.utils import num_tokens_from_string, truncate
 from rag.utils.redis_conn import REDIS_CONN
 from rag.utils.storage_factory import STORAGE_IMPL
 from graphrag.utils import chat_limiter
@@ -404,7 +404,7 @@ async def embedding(docs, mdl, parser_config=None, callback=None):
 
     cnts_ = np.array([])
     for i in range(0, len(cnts), batch_size):
-        vts, c = await trio.to_thread.run_sync(lambda: mdl.encode(cnts[i: i + batch_size]))
+        vts, c = await trio.to_thread.run_sync(lambda: mdl.encode([truncate(c, mdl.max_length-10) for c in cnts[i: i + batch_size]]))
         if len(cnts_) == 0:
             cnts_ = vts
         else: