diff --git a/src/memos/configs/embedder.py b/src/memos/configs/embedder.py
index 2d5dc61fe..fa14d8c7f 100644
--- a/src/memos/configs/embedder.py
+++ b/src/memos/configs/embedder.py
@@ -24,6 +24,10 @@ class ArkEmbedderConfig(BaseEmbedderConfig):
         default="https://ark.cn-beijing.volces.com/api/v3/", description="Base URL for Ark API"
     )
     chunk_size: int = Field(default=1, description="Chunk size for Ark API")
+    muiti_modal: bool = Field(
+        default=False,
+        description="Whether to use multi-modal embedding (text + image) with Ark",
+    )
 
 
 class SenTranEmbedderConfig(BaseEmbedderConfig):
diff --git a/src/memos/embedders/ark.py b/src/memos/embedders/ark.py
index cc8fba809..f74846658 100644
--- a/src/memos/embedders/ark.py
+++ b/src/memos/embedders/ark.py
@@ -44,10 +44,26 @@ def embed(self, texts: list[str]) -> list[list[float]]:
         Returns:
             List of embeddings, each represented as a list of floats.
         """
-        texts_input = [
-            MultimodalEmbeddingContentPartTextParam(text=text, type="text") for text in texts
-        ]
-        return self.multimodal_embeddings(texts_input, chunk_size=self.config.chunk_size)
+        if self.config.muiti_modal:
+            texts_input = [
+                MultimodalEmbeddingContentPartTextParam(text=text, type="text") for text in texts
+            ]
+            return self.multimodal_embeddings(inputs=texts_input, chunk_size=self.config.chunk_size)
+        return self.text_embedding(texts, chunk_size=self.config.chunk_size)
+
+    def text_embedding(self, inputs: list[str], chunk_size: int | None = None) -> list[list[float]]:
+        chunk_size_ = chunk_size or self.config.chunk_size
+        embeddings: list[list[float]] = []
+        for i in range(0, len(inputs), chunk_size_):
+            response = self.client.embeddings.create(
+                model=self.config.model_name_or_path,
+                input=inputs[i : i + chunk_size_],
+            )
+
+            data = [response.data] if isinstance(response.data, dict) else response.data
+            embeddings.extend(r.embedding for r in data)
+
+        return embeddings
 
     def multimodal_embeddings(
         self, inputs: list[EmbeddingInputParam], chunk_size: int | None = None
diff --git a/src/memos/embedders/universal_api.py b/src/memos/embedders/universal_api.py
index a2863cf5d..cd797fe26 100644
--- a/src/memos/embedders/universal_api.py
+++ b/src/memos/embedders/universal_api.py
@@ -1,4 +1,5 @@
 from openai import OpenAI as OpenAIClient
+from openai import AzureOpenAI as AzureClient
 
 from memos.configs.embedder import UniversalAPIEmbedderConfig
 from memos.embedders.base import BaseEmbedder
@@ -11,11 +12,17 @@ def __init__(self, config: UniversalAPIEmbedderConfig):
 
         if self.provider == "openai":
             self.client = OpenAIClient(api_key=config.api_key, base_url=config.base_url)
+        elif self.provider == "azure":
+            self.client = AzureClient(
+                azure_endpoint=config.base_url,
+                api_version="2024-03-01-preview",
+                api_key=config.api_key,
+            )
         else:
             raise ValueError(f"Unsupported provider: {self.provider}")
 
     def embed(self, texts: list[str]) -> list[list[float]]:
-        if self.provider == "openai":
+        if self.provider == "openai" or self.provider == "azure":
             response = self.client.embeddings.create(
                 model=getattr(self.config, "model_name_or_path", "text-embedding-3-large"),
                 input=texts,