Add More backend (#99)

* 1. 豆包支持非多模态模型
2. embedder支持Azure backend

* fix typo

* add example

Author: Gaojianli

examples/basic_modules/embedder.py

@@ -48,7 +48,7 @@
 print("Scenario 3 HF embedding shape:", len(embedding_hf[0]))
 print("==" * 20)
 
-# === Scenario 4: Using UniversalAPIEmbedder ===
+# === Scenario 4: Using UniversalAPIEmbedder(OpenAI) ===
 
 config_api = EmbedderConfigFactory.model_validate(
     {
@@ -66,3 +66,22 @@
 embedding_api = embedder_api.embed([text_api])
 print("Scenario 4: OpenAI API embedding vector length:", len(embedding_api[0]))
 print("Embedding preview:", embedding_api[0][:10])
+
+# === Scenario 5: Using UniversalAPIEmbedder(Azure) ===
+
+config_api = EmbedderConfigFactory.model_validate(
+    {
+        "backend": "universal_api",
+        "config": {
+            "provider": "azure",
+            "api_key": "<YOUR_AZURE_KEY>",
+            "model_name_or_path": "text-embedding-3-large",
+            "base_url": "https://open.azure.com/openapi/online/v2/",
+        },
+    }
+)
+embedder_api = EmbedderFactory.from_config(config_api)
+text_api = "This is a sample text for embedding generation using Azure API."
+embedding_api = embedder_api.embed([text_api])
+print("Scenario 5: Azure API embedding vector length:", len(embedding_api[0]))
+print("Embedding preview:", embedding_api[0][:10])

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")
+    multi_modal: bool = Field(
+        default=False,
+        description="Whether to use multi-modal embedding (text + image) with Ark",
+    )
 
 
 class SenTranEmbedderConfig(BaseEmbedderConfig):

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.multi_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

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,