(feat) Vertex AI - add support for fine tuned embedding models (#6749)

* fix use fine tuned vertex embedding models

* test_vertex_embedding_url

* add _transform_openai_request_to_fine_tuned_embedding_request

* add _transform_openai_request_to_fine_tuned_embedding_request

* add transform_openai_request_to_vertex_embedding_request

* add _transform_vertex_response_to_openai_for_fine_tuned_models

* test_vertexai_embedding for ft models

* fix test_vertexai_embedding_finetuned

* doc fine tuned / custom embedding models

* fix test test_partner_models_httpx

docs/my-website/docs/providers/vertex.md

@@ -1562,6 +1562,10 @@ curl http://0.0.0.0:4000/v1/chat/completions \
 ## **Embedding Models**
 
 #### Usage - Embedding
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
 ```python
 import litellm
 from litellm import embedding
@@ -1574,6 +1578,49 @@ response = embedding(
 )
 print(response)
 ```
+</TabItem>
+
+<TabItem value="proxy" label="LiteLLM PROXY">
+
+
+1. Add model to config.yaml
+```yaml
+model_list:
+  - model_name: snowflake-arctic-embed-m-long-1731622468876
+    litellm_params:
+      model: vertex_ai/<your-model-id>
+      vertex_project: "adroit-crow-413218"
+      vertex_location: "us-central1"
+      vertex_credentials: adroit-crow-413218-a956eef1a2a8.json 
+
+litellm_settings:
+  drop_params: True
+```
+
+2. Start Proxy 
+
+```
+$ litellm --config /path/to/config.yaml
+```
+
+3. Make Request using OpenAI Python SDK, Langchain Python SDK
+
+```python
+import openai
+
+client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
+
+response = client.embeddings.create(
+    model="snowflake-arctic-embed-m-long-1731622468876", 
+    input = ["good morning from litellm", "this is another item"],
+)
+
+print(response)
+```
+
+
+</TabItem>
+</Tabs>
 
 #### Supported Embedding Models
 All models listed [here](https://github.com/BerriAI/litellm/blob/57f37f743886a0249f630a6792d49dffc2c5d9b7/model_prices_and_context_window.json#L835) are supported
@@ -1589,6 +1636,7 @@ All models listed [here](https://github.com/BerriAI/litellm/blob/57f37f743886a02
 | textembedding-gecko@003 | `embedding(model="vertex_ai/textembedding-gecko@003", input)` | 
 | text-embedding-preview-0409 | `embedding(model="vertex_ai/text-embedding-preview-0409", input)` |
 | text-multilingual-embedding-preview-0409 | `embedding(model="vertex_ai/text-multilingual-embedding-preview-0409", input)` | 
+| Fine-tuned OR Custom Embedding models | `embedding(model="vertex_ai/<your-model-id>", input)` | 
 
 ### Supported OpenAI (Unified) Params
 

litellm/llms/vertex_ai_and_google_ai_studio/common_utils.py

@@ -89,6 +89,9 @@ def _get_vertex_url(
     elif mode == "embedding":
         endpoint = "predict"
         url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}"
+        if model.isdigit():
+            # https://us-central1-aiplatform.googleapis.com/v1/projects/$PROJECT_ID/locations/us-central1/endpoints/$ENDPOINT_ID:predict
+            url = f"https://{vertex_location}-aiplatform.googleapis.com/{vertex_api_version}/projects/{vertex_project}/locations/{vertex_location}/endpoints/{model}:{endpoint}"
 
     if not url or not endpoint:
         raise ValueError(f"Unable to get vertex url/endpoint for mode: {mode}")

litellm/llms/vertex_ai_and_google_ai_studio/vertex_embeddings/embedding_handler.py

@@ -96,7 +96,7 @@ class VertexEmbedding(VertexBase):
         headers = self.set_headers(auth_header=auth_header, extra_headers=extra_headers)
         vertex_request: VertexEmbeddingRequest = (
             litellm.vertexAITextEmbeddingConfig.transform_openai_request_to_vertex_embedding_request(
-                input=input, optional_params=optional_params
+                input=input, optional_params=optional_params, model=model
             )
         )
 
@@ -188,7 +188,7 @@ class VertexEmbedding(VertexBase):
         headers = self.set_headers(auth_header=auth_header, extra_headers=extra_headers)
         vertex_request: VertexEmbeddingRequest = (
             litellm.vertexAITextEmbeddingConfig.transform_openai_request_to_vertex_embedding_request(
-                input=input, optional_params=optional_params
+                input=input, optional_params=optional_params, model=model
             )
         )
 

litellm/llms/vertex_ai_and_google_ai_studio/vertex_embeddings/transformation.py

@@ -101,11 +101,16 @@ class VertexAITextEmbeddingConfig(BaseModel):
         return optional_params
 
     def transform_openai_request_to_vertex_embedding_request(
-        self, input: Union[list, str], optional_params: dict
+        self, input: Union[list, str], optional_params: dict, model: str
     ) -> VertexEmbeddingRequest:
         """
         Transforms an openai request to a vertex embedding request.
         """
+        if model.isdigit():
+            return self._transform_openai_request_to_fine_tuned_embedding_request(
+                input, optional_params, model
+            )
+
         vertex_request: VertexEmbeddingRequest = VertexEmbeddingRequest()
         vertex_text_embedding_input_list: List[TextEmbeddingInput] = []
         task_type: Optional[TaskType] = optional_params.get("task_type")
@@ -125,6 +130,47 @@ class VertexAITextEmbeddingConfig(BaseModel):
 
         return vertex_request
 
+    def _transform_openai_request_to_fine_tuned_embedding_request(
+        self, input: Union[list, str], optional_params: dict, model: str
+    ) -> VertexEmbeddingRequest:
+        """
+        Transforms an openai request to a vertex fine-tuned embedding request.
+
+        Vertex Doc: https://console.cloud.google.com/vertex-ai/model-garden?hl=en&project=adroit-crow-413218&pageState=(%22galleryStateKey%22:(%22f%22:(%22g%22:%5B%5D,%22o%22:%5B%5D),%22s%22:%22%22))
+        Sample Request:
+
+        ```json
+        {
+            "instances" : [
+                {
+                "inputs": "How would the Future of AI in 10 Years look?",
+                "parameters": {
+                    "max_new_tokens": 128,
+                    "temperature": 1.0,
+                    "top_p": 0.9,
+                    "top_k": 10
+                }
+                }
+            ]
+        }
+        ```
+        """
+        vertex_request: VertexEmbeddingRequest = VertexEmbeddingRequest()
+        vertex_text_embedding_input_list: List[TextEmbeddingFineTunedInput] = []
+        if isinstance(input, str):
+            input = [input]  # Convert single string to list for uniform processing
+
+        for text in input:
+            embedding_input = TextEmbeddingFineTunedInput(inputs=text)
+            vertex_text_embedding_input_list.append(embedding_input)
+
+        vertex_request["instances"] = vertex_text_embedding_input_list
+        vertex_request["parameters"] = TextEmbeddingFineTunedParameters(
+            **optional_params
+        )
+
+        return vertex_request
+
     def create_embedding_input(
         self,
         content: str,
@@ -157,6 +203,11 @@ class VertexAITextEmbeddingConfig(BaseModel):
         """
         Transforms a vertex embedding response to an openai response.
         """
+        if model.isdigit():
+            return self._transform_vertex_response_to_openai_for_fine_tuned_models(
+                response, model, model_response
+            )
+
         _predictions = response["predictions"]
 
         embedding_response = []
@@ -181,3 +232,35 @@ class VertexAITextEmbeddingConfig(BaseModel):
         )
         setattr(model_response, "usage", usage)
         return model_response
+
+    def _transform_vertex_response_to_openai_for_fine_tuned_models(
+        self, response: dict, model: str, model_response: litellm.EmbeddingResponse
+    ) -> litellm.EmbeddingResponse:
+        """
+        Transforms a vertex fine-tuned model embedding response to an openai response format.
+        """
+        _predictions = response["predictions"]
+
+        embedding_response = []
+        # For fine-tuned models, we don't get token counts in the response
+        input_tokens = 0
+
+        for idx, embedding_values in enumerate(_predictions):
+            embedding_response.append(
+                {
+                    "object": "embedding",
+                    "index": idx,
+                    "embedding": embedding_values[
+                        0
+                    ],  # The embedding values are nested one level deeper
+                }
+            )
+
+        model_response.object = "list"
+        model_response.data = embedding_response
+        model_response.model = model
+        usage = Usage(
+            prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
+        )
+        setattr(model_response, "usage", usage)
+        return model_response

litellm/llms/vertex_ai_and_google_ai_studio/vertex_embeddings/types.py

@@ -23,14 +23,27 @@ class TextEmbeddingInput(TypedDict, total=False):
     title: Optional[str]
 
 
+# Fine-tuned models require a different input format
+# Ref: https://console.cloud.google.com/vertex-ai/model-garden?hl=en&project=adroit-crow-413218&pageState=(%22galleryStateKey%22:(%22f%22:(%22g%22:%5B%5D,%22o%22:%5B%5D),%22s%22:%22%22))
+class TextEmbeddingFineTunedInput(TypedDict, total=False):
+    inputs: str
+
+
+class TextEmbeddingFineTunedParameters(TypedDict, total=False):
+    max_new_tokens: Optional[int]
+    temperature: Optional[float]
+    top_p: Optional[float]
+    top_k: Optional[int]
+
+
 class EmbeddingParameters(TypedDict, total=False):
     auto_truncate: Optional[bool]
     output_dimensionality: Optional[int]
 
 
 class VertexEmbeddingRequest(TypedDict, total=False):
-    instances: List[TextEmbeddingInput]
+    instances: Union[List[TextEmbeddingInput], List[TextEmbeddingFineTunedInput]]
-    parameters: Optional[EmbeddingParameters]
+    parameters: Optional[Union[EmbeddingParameters, TextEmbeddingFineTunedParameters]]
 
 
 # Example usage:

tests/llm_translation/test_vertex.py

@@ -16,6 +16,7 @@ import pytest
 import litellm
 from litellm import get_optional_params
 from litellm.llms.custom_httpx.http_handler import HTTPHandler
+import httpx
 
 
 def test_completion_pydantic_obj_2():
@@ -1317,3 +1318,39 @@ def test_image_completion_request(image_url):
         mock_post.assert_called_once()
         print("mock_post.call_args.kwargs['json']", mock_post.call_args.kwargs["json"])
         assert mock_post.call_args.kwargs["json"] == expected_request_body
+
+
+@pytest.mark.parametrize(
+    "model, expected_url",
+    [
+        (
+            "textembedding-gecko@001",
+            "https://us-central1-aiplatform.googleapis.com/v1/projects/project-id/locations/us-central1/publishers/google/models/textembedding-gecko@001:predict",
+        ),
+        (
+            "123456789",
+            "https://us-central1-aiplatform.googleapis.com/v1/projects/project-id/locations/us-central1/endpoints/123456789:predict",
+        ),
+    ],
+)
+def test_vertex_embedding_url(model, expected_url):
+    """
+    Test URL generation for embedding models, including numeric model IDs (fine-tuned models
+
+    Relevant issue: https://github.com/BerriAI/litellm/issues/6482
+
+    When a fine-tuned embedding model is used, the URL is different from the standard one.
+    """
+    from litellm.llms.vertex_ai_and_google_ai_studio.common_utils import _get_vertex_url
+
+    url, endpoint = _get_vertex_url(
+        mode="embedding",
+        model=model,
+        stream=False,
+        vertex_project="project-id",
+        vertex_location="us-central1",
+        vertex_api_version="v1",
+    )
+
+    assert url == expected_url
+    assert endpoint == "predict"

tests/local_testing/test_amazing_vertex_completion.py

@@ -18,6 +18,8 @@ import json
 import os
 import tempfile
 from unittest.mock import AsyncMock, MagicMock, patch
+from respx import MockRouter
+import httpx
 
 import pytest
 
@@ -973,6 +975,7 @@ async def test_partner_models_httpx(model, sync_mode):
         data = {
             "model": model,
             "messages": messages,
+            "timeout": 10,
         }
         if sync_mode:
             response = litellm.completion(**data)
@@ -986,6 +989,8 @@ async def test_partner_models_httpx(model, sync_mode):
         assert isinstance(response._hidden_params["response_cost"], float)
     except litellm.RateLimitError as e:
         pass
+    except litellm.Timeout as e:
+        pass
     except litellm.InternalServerError as e:
         pass
     except Exception as e:
@@ -3051,3 +3056,70 @@ def test_custom_api_base(api_base):
         assert url == api_base + ":"
     else:
         assert url == test_endpoint
+
+
+@pytest.mark.asyncio
+@pytest.mark.respx
+async def test_vertexai_embedding_finetuned(respx_mock: MockRouter):
+    """
+    Tests that:
+    - Request URL and body are correctly formatted for Vertex AI embeddings
+    - Response is properly parsed into litellm's embedding response format
+    """
+    load_vertex_ai_credentials()
+    litellm.set_verbose = True
+
+    # Test input
+    input_text = ["good morning from litellm", "this is another item"]
+
+    # Expected request/response
+    expected_url = "https://us-central1-aiplatform.googleapis.com/v1/projects/633608382793/locations/us-central1/endpoints/1004708436694269952:predict"
+    expected_request = {
+        "instances": [
+            {"inputs": "good morning from litellm"},
+            {"inputs": "this is another item"},
+        ],
+        "parameters": {},
+    }
+
+    mock_response = {
+        "predictions": [
+            [[-0.000431762, -0.04416759, -0.03443353]],  # Truncated embedding vector
+            [[-0.000431762, -0.04416759, -0.03443353]],  # Truncated embedding vector
+        ],
+        "deployedModelId": "2275167734310371328",
+        "model": "projects/633608382793/locations/us-central1/models/snowflake-arctic-embed-m-long-1731622468876",
+        "modelDisplayName": "snowflake-arctic-embed-m-long-1731622468876",
+        "modelVersionId": "1",
+    }
+
+    # Setup mock request
+    mock_request = respx_mock.post(expected_url).mock(
+        return_value=httpx.Response(200, json=mock_response)
+    )
+
+    # Make request
+    response = await litellm.aembedding(
+        vertex_project="633608382793",
+        model="vertex_ai/1004708436694269952",
+        input=input_text,
+    )
+
+    # Assert request was made correctly
+    assert mock_request.called
+    request_body = json.loads(mock_request.calls[0].request.content)
+    print("\n\nrequest_body", request_body)
+    print("\n\nexpected_request", expected_request)
+    assert request_body == expected_request
+
+    # Assert response structure
+    assert response is not None
+    assert hasattr(response, "data")
+    assert len(response.data) == len(input_text)
+
+    # Assert embedding structure
+    for embedding in response.data:
+        assert "embedding" in embedding
+        assert isinstance(embedding["embedding"], list)
+        assert len(embedding["embedding"]) > 0
+        assert all(isinstance(x, float) for x in embedding["embedding"])