feat: add openai-compatible embedding providers

litellm/constants.py

@@ -529,6 +529,9 @@ LITELLM_CHAT_PROVIDERS: Final = [
     "gdc",
     "xai",
     "custom_openai",
+    "byteplus",
+    "digitalocean",
+    "siliconflow",
     "text-completion-openai",
     "cohere",
     "cohere_chat",
@@ -847,6 +850,9 @@ openai_compatible_providers: Final[list] = [
     "cometapi",
     "clarifai",
     "docker_model_runner",
+    "byteplus",
+    "digitalocean",
+    "siliconflow",
     "ragflow",
     "pinstripes",  # Pinstripes - JSON-configured provider
     "darkbloom",
@@ -876,6 +882,9 @@ openai_text_completion_compatible_providers: Final[list] = [  # providers that s
     "lambda_ai",
     "hyperbolic",
     "wandb",
+    "byteplus",
+    "digitalocean",
+    "siliconflow",
 ]
 _openai_like_providers: Final[list] = [
     "predibase",

litellm/main.py

@@ -6222,6 +6222,9 @@ def embedding(
             or custom_llm_provider == "together_ai"
             or custom_llm_provider == "nvidia_nim"
             or custom_llm_provider == "litellm_proxy"
+            or custom_llm_provider == "byteplus"
+            or custom_llm_provider == "digitalocean"
+            or custom_llm_provider == "siliconflow"
             or (model in litellm.open_ai_embedding_models and custom_llm_provider is None)
         ):
             api_base = (

litellm/types/utils.py

@@ -3664,6 +3664,9 @@ class LlmProviders(str, Enum):
     ANTHROPIC = "anthropic"
     ANTHROPIC_TEXT = "anthropic_text"
     BYTEZ = "bytez"
+    BYTEPLUS = "byteplus"
+    DIGITALOCEAN = "digitalocean"
+    SILICONFLOW = "siliconflow"
     REPLICATE = "replicate"
     REDUCTO = "reducto"
     RUNWAYML = "runwayml"

tests/test_litellm/test_openai_compatible_embedding_providers.py

@@ -0,0 +1,34 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+import litellm
+
+
+@pytest.mark.parametrize("provider", ["byteplus", "digitalocean", "siliconflow"])
+def test_openai_compatible_provider_embedding_dispatch(provider):
+    mock_response = MagicMock()
+    mock_response.parse.return_value = MagicMock(
+        model_dump=lambda: {
+            "data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}],
+            "model": "test-embedding",
+            "object": "list",
+            "usage": {"prompt_tokens": 1, "total_tokens": 1},
+        }
+    )
+    mock_response.headers = {}
+
+    with patch("litellm.llms.openai.openai.OpenAIChatCompletion._get_openai_client") as mock_get_client:
+        mock_client = MagicMock()
+        mock_get_client.return_value = mock_client
+        mock_client.embeddings.with_raw_response.create.return_value = mock_response
+
+        response = litellm.embedding(
+            model=f"{provider}/test-embedding",
+            input="Hello world",
+            api_key="test-key",
+            api_base="http://test.example/v1",
+        )
+
+    assert response.data[0]["embedding"] == [0.1, 0.2, 0.3]
+    assert mock_client.embeddings.with_raw_response.create.called