diff --git a/memory_scope/models/base_model.py b/memory_scope/models/base_model.py
index af415976..31e56dab 100644
--- a/memory_scope/models/base_model.py
+++ b/memory_scope/models/base_model.py
@@ -3,6 +3,7 @@ import inspect
 import time
 from abc import abstractmethod, ABCMeta
 
+from memory_scope.enumeration.model_enum import ModelEnum
 from memory_scope.models import MODEL_REGISTRY
 from memory_scope.models.response import ModelResponse, ModelResponseGen
 from memory_scope.utils.logger import Logger
@@ -10,6 +11,7 @@ from memory_scope.utils.timer import Timer
 
 
 class BaseModel(metaclass=ABCMeta):
+    model_type: ModelEnum | None = None
 
     def __init__(self,
                  model_name: str,
diff --git a/memory_scope/models/base_generation_model.py b/memory_scope/models/llama_index_generation_model.py
similarity index 83%
rename from memory_scope/models/base_generation_model.py
rename to memory_scope/models/llama_index_generation_model.py
index c989ff51..0cbfcd47 100644
--- a/memory_scope/models/base_generation_model.py
+++ b/memory_scope/models/llama_index_generation_model.py
@@ -1,40 +1,25 @@
 from typing import List, Dict
 
-#from llama_index.llms.dashscope import DashScope as DashScopeLLM
-from llama_index.llms.dashscope import DashScope
-
 from llama_index.core.base.llms.types import ChatMessage
 from llama_index.core.base.llms.types import (
     ChatResponse,
     CompletionResponse,
 )
+from llama_index.llms.dashscope import DashScope
+
+from memory_scope.enumeration.model_enum import ModelEnum
 from memory_scope.models import MODEL_REGISTRY
 from memory_scope.models.base_model import BaseModel
 from memory_scope.models.response import ModelResponse, ModelResponseGen
-from memory_scope.utils.timer import Timer
 
 
-class BaseGenerationModel(BaseModel):
+class LlamaIndexGenerationModel(BaseModel):
+    model_type: ModelEnum = ModelEnum.GENERATION_MODEL
+
     MODEL_REGISTRY.batch_register([
         DashScope,
     ])
 
-    def before_call(self, **kwargs) -> None:
-        pass
-
-    def after_call(self, model_response: ModelResponse | ModelResponseGen,
-                   **kwargs) -> ModelResponse | ModelResponseGen:
-        pass
-
-    def _call(self, stream: bool = False, **kwargs) -> ModelResponse | ModelResponseGen:
-        pass
-
-    async def _async_call(self, **kwargs) -> ModelResponse:
-        pass
-
-
-class LLILLM(BaseGenerationModel):
-
     def before_call(self, **kwargs) -> None:
         prompt: str = kwargs.pop("prompt", "")
         messages: List[Dict[str, str]] = kwargs.pop("messages", [])
@@ -91,3 +76,5 @@ class LLILLM(BaseGenerationModel):
             results.details = e
         return results
 
+    async def _async_call(self, **kwargs) -> ModelResponse:
+        pass
diff --git a/memory_scope/models/response.py b/memory_scope/models/response.py
index 52016c89..34fed4df 100644
--- a/memory_scope/models/response.py
+++ b/memory_scope/models/response.py
@@ -2,21 +2,25 @@ from typing import Generator, List, Dict, Any
 
 from pydantic import BaseModel, Field
 
+from memory_scope.enumeration.model_enum import ModelEnum
+
 
 class ModelResponse(BaseModel):
     text: str = Field("", description="")
 
-    embedding_results:  List[List[float]] = Field([], description="")
+    embedding_results: List[List[float]] | List[float] = Field([], description="embedding result")
 
-    #rank_scores: Dict[int, float] = Field({}, description="The rank scores of each documents.")
-    rank_scores: List[Dict[str, Any]] = Field({}, description="The rank scores of each documents.")
-    # [{"document": "xxx", "score": 0.5}, {{"document": "yyy", "score": 0.3}}]
-    model_type: str = Field("", description="One of LLM, EMB, RANK.")
+    rank_scores: List[Dict[int, float]] = Field([], description="The rank scores of each documents. "
+                                                                "key: index, value: rank score")
+
+    model_type: ModelEnum = Field("", description="One of LLM, EMB, RANK.")
 
     status: bool = Field(True, description="Indicates whether the model call was successful.")
 
-    details: str = Field("", description=("The details information for model call, \
-                         usually for storage of raw response or failure messages."))
+    details: str = Field("", description="The details information for model call, "
+                                         "usually for storage of raw response or failure messages.")
+
+    raw: Any = Field("", description="raw response from model call")
+
 
-    raw: Any = Field("", description=("raw response from model call"))
 ModelResponseGen = Generator[ModelResponse, None, None]
diff --git a/tests/models/test_models_lli_llm.py b/tests/models/test_models_lli_llm.py
index 3fac66f8..78c7e143 100644
--- a/tests/models/test_models_lli_llm.py
+++ b/tests/models/test_models_lli_llm.py
@@ -1,5 +1,7 @@
 import unittest
-from memory_scope.models.base_generation_model import LLILLM
+
+from memory_scope.models.llama_index_generation_model import LlamaIndexGenerationModel
+
 
 class TestLLILLM(unittest.TestCase):
     """Tests for LLIEmbedding"""
@@ -8,9 +10,9 @@ class TestLLILLM(unittest.TestCase):
         config = {
             "method_type": "DashScope",
             "model_name": "qwen-max",
-            "clazz": "models.base_generation_model"
+            "clazz": "models.llama_index_generation_model"
         }
-        self.llm = LLILLM(**config)
+        self.llm = LlamaIndexGenerationModel(**config)
 
     def test_llm_prompt(self):
         prompt = "你是谁？"
@@ -21,10 +23,10 @@ class TestLLILLM(unittest.TestCase):
         print(ans.text)
 
     def test_llm_messages(self):
-        messages = [{"role": "system", "content": "you are a helpful assistant."}, 
+        messages = [{"role": "system", "content": "you are a helpful assistant."},
                     {"role": "user", "content": "你是谁？"}]
         ans = self.llm.call(
             stream=False,
             messages=messages
         )
-        print(ans.text)
\ No newline at end of file
+        print(ans.text)