diff --git a/modelscope_agent/rag/emb/__init__.py b/modelscope_agent/rag/emb/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/modelscope_agent/rag/emb/dashscope.py b/modelscope_agent/rag/emb/dashscope.py
deleted file mode 100644
index 0bf36dd5..00000000
--- a/modelscope_agent/rag/emb/dashscope.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import os
-from enum import Enum
-from http import HTTPStatus
-from typing import Any, List, Optional
-
-import dashscope
-from llama_index.core.base.embeddings.base import (DEFAULT_EMBED_BATCH_SIZE,
-                                                   BaseEmbedding)
-from llama_index.core.callbacks import CallbackManager
-
-# Enums for validation and type safety
-DashscopeModelName = [
-    'text-embedding-v1',
-    'text-embedding-v2',
-]
-
-
-# Assuming BaseEmbedding is a Pydantic model and handles its own initializations
-class DashscopeEmbedding(BaseEmbedding):
-    """DashscopeEmbedding uses the dashscope API to generate embeddings for text."""
-
-    def __init__(
-        self,
-        model_name: str = 'text-embedding-v2',
-        embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
-        callback_manager: Optional[CallbackManager] = None,
-    ):
-        """
-        A class representation for generating embeddings using the dashscope API.
-
-        Args:
-            model_name (str): The name of the model to be used for generating embeddings. The class ensures that
-                          this model is supported and that the input type provided is compatible with the model.
-        """
-
-        assert os.environ.get(
-            'DASHSCOPE_API_KEY',
-            None), 'DASHSCOPE_API_KEY should be set in environ.'
-
-        # Validate model_name and input_type
-        if model_name not in DashscopeModelName:
-            raise ValueError(f'model {model_name} is not supported.')
-
-        super().__init__(
-            model_name=model_name,
-            embed_batch_size=embed_batch_size,
-            callback_manager=callback_manager,
-        )
-
-    @classmethod
-    def class_name(cls) -> str:
-        return 'DashscopeEmbedding'
-
-    def _embed(self,
-               texts: List[str],
-               text_type='document') -> List[List[float]]:
-        """Embed sentences using dashscope."""
-        resp = dashscope.TextEmbedding.call(
-            input=texts,
-            model=self.model_name,
-            text_type=text_type,
-        )
-        if resp.status_code == HTTPStatus.OK:
-            res = resp.output['embeddings']
-        else:
-            raise ValueError(f'call dashscope api failed: {resp}')
-
-        return [list(map(float, e['embedding'])) for e in res]
-
-    def _get_query_embedding(self, query: str) -> List[float]:
-        """Get query embedding."""
-        return self._embed([query], text_type='query')[0]
-
-    async def _aget_query_embedding(self, query: str) -> List[float]:
-        """Get query embedding async."""
-        return self._get_query_embedding(query)
-
-    def _get_text_embedding(self, text: str) -> List[float]:
-        """Get text embedding."""
-        return self._embed([text], text_type='document')[0]
-
-    async def _aget_text_embedding(self, text: str) -> List[float]:
-        """Get text embedding async."""
-        return self._get_text_embedding(text)
-
-    def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
-        """Get text embeddings."""
-        return self._embed(texts, text_type='document')