diff --git a/src/aios_kernel/knowledge_base.py b/src/aios_kernel/knowledge_base.py
index d2debd8..b528192 100644
--- a/src/aios_kernel/knowledge_base.py
+++ b/src/aios_kernel/knowledge_base.py
@@ -20,6 +20,7 @@ class KnowledgeBase:
     def __singleton_init__(self) -> None:
         self.store = KnowledgeStore()
         self.compute_kernel = ComputeKernel.get_instance()
+        self._default_text_model = "all-MiniLM-L6-v2"
 
     async def __embedding_document(self, document: DocumentObject):
         for chunk_id in document.get_chunk_list():
@@ -28,8 +29,8 @@ class KnowledgeBase:
                 raise ValueError(f"text chunk not found: {chunk_id}")
         
             text = chunk.read().decode("utf-8")
-            vector = await self.compute_kernel.do_text_embedding(text)
-            await self.store.get_vector_store("default").insert(vector, chunk_id)
+            vector = await self.compute_kernel.do_text_embedding(text, self._default_text_model)
+            await self.store.get_vector_store(self._default_text_model).insert(vector, chunk_id)
 
     async def __embedding_image(self, image: ImageObject):
         desc = {}
@@ -39,8 +40,8 @@ class KnowledgeBase:
             desc["exif"] = image.get_exif()
         if not not image.get_tags():
             desc["tags"] = image.get_tags()
-        vector = await self.compute_kernel.do_text_embedding(json.dumps(desc))
-        await self.store.get_vector_store("default").insert(vector, image.calculate_id())
+        vector = await self.compute_kernel.do_text_embedding(json.dumps(desc), self._default_text_model)
+        await self.store.get_vector_store(self._default_text_model).insert(vector, image.calculate_id())
 
     async def __embedding_video(self, vedio: VideoObject):
         desc = {}
@@ -50,8 +51,8 @@ class KnowledgeBase:
             desc["info"] = vedio.get_info()
         if not not vedio.get_tags():
             desc["tags"] = vedio.get_tags()
-        vector = await self.compute_kernel.do_text_embedding(json.dumps(desc))
-        await self.store.get_vector_store("default").insert(vector, vedio.calculate_id())
+        vector = await self.compute_kernel.do_text_embedding(json.dumps(desc), self._default_text_model)
+        await self.store.get_vector_store(self._default_text_model).insert(vector, vedio.calculate_id())
 
     async def __embedding_rich_text(self, rich_text: RichTextObject):
         for document_id in rich_text.get_documents().values():
@@ -68,8 +69,8 @@ class KnowledgeBase:
             await self.__embedding_rich_text(rich_text)
 
     async def __embedding_email(self, email: EmailObject):
-        vector = await self.compute_kernel.do_text_embedding(json.dumps(email.get_desc()))
-        await self.store.get_vector_store("default").insert(vector, email.calculate_id())
+        vector = await self.compute_kernel.do_text_embedding(json.dumps(email.get_desc()), self._default_text_model)
+        await self.store.get_vector_store(self._default_text_model).insert(vector, email.calculate_id())
         await self.__embedding_rich_text(email.get_rich_text())
 
 
@@ -172,8 +173,8 @@ class KnowledgeBase:
         results = []
         for msg in prompt.messages:
             if msg["role"] == "user":
-                vector = await self.compute_kernel.do_text_embedding(msg["content"])
-                object_ids = await self.store.get_vector_store("default").query(vector, 10)
+                vector = await self.compute_kernel.do_text_embedding(msg["content"], self._default_text_model)
+                object_ids = await self.store.get_vector_store(self._default_text_model).query(vector, 10)
                 results.extend(object_ids)
         return results
 
diff --git a/src/aios_kernel/local_st_compute_node.py b/src/aios_kernel/local_st_compute_node.py
index e73b9f9..bc9f4af 100644
--- a/src/aios_kernel/local_st_compute_node.py
+++ b/src/aios_kernel/local_st_compute_node.py
@@ -89,7 +89,7 @@ class LocalSentenceTransformer_ComputeNode(Queue_ComputeNode):
 
     def is_support(self, task: ComputeTask) -> bool:
         return task.task_type == ComputeTaskType.TEXT_EMBEDDING and (
-            not task.params["model_name"] or task.params["model_name"] == "llama"
+            not task.params["model_name"] or task.params["model_name"] == "all-MiniLM-L6-v2"
         )
 
     def is_local(self) -> bool:
diff --git a/src/knowledge/data/writer.py b/src/knowledge/data/writer.py
index 0a87ac1..0381bc6 100644
--- a/src/knowledge/data/writer.py
+++ b/src/knowledge/data/writer.py
@@ -3,14 +3,14 @@ import hashlib
 import re
 import tiktoken
 import logging
-from typing import Tuple, List
+from typing import Callable, Iterable, Optional, Tuple, List
 from .chunk_store import ChunkStore
 from .chunk import ChunkID, PositionFileRange, PositionType
 from ..object import HashValue
 from .tracker import ChunkTracker
 from .chunk_list import ChunkList
 
-def _join_docs(self, docs: List[str], separator: str) -> Optional[str]:
+def _join_docs(docs: List[str], separator: str) -> Optional[str]:
     text = separator.join(docs)
     text = text.strip()
     if text == "":
@@ -19,7 +19,6 @@ def _join_docs(self, docs: List[str], separator: str) -> Optional[str]:
         return text
 
 def _merge_splits(
-        self, 
         splits: Iterable[str], 
         separator: str, 
         chunk_size: int,