fix(embeddings_handler.py): initial working commit for google ai studio text embeddings /embedContent endpoint

litellm/llms/vertex_ai_and_google_ai_studio/gemini/embeddings_handler.py

@@ -10,7 +10,14 @@ import httpx
 import litellm
 from litellm import EmbeddingResponse
 from litellm.llms.custom_httpx.http_handler import HTTPHandler
+from litellm.types.llms.vertex_ai import (
+    VertexAITextEmbeddingsRequestBody,
+    VertexAITextEmbeddingsResponseObject,
+)
+from litellm.types.utils import Embedding
+from litellm.utils import get_formatted_prompt
 
+from .embeddings_transformation import transform_openai_input_gemini_content
 from .vertex_and_google_ai_studio_gemini import VertexLLM
 
 
@@ -34,7 +41,7 @@ class GoogleEmbeddings(VertexLLM):
         timeout=300,
         client=None,
     ) -> EmbeddingResponse:
-        return model_response
+
         auth_header, url = self._get_token_and_url(
             model=model,
             gemini_api_key=api_key,
@@ -63,59 +70,58 @@ class GoogleEmbeddings(VertexLLM):
 
         optional_params = optional_params or {}
 
-        # request_data = VertexMultimodalEmbeddingRequest()
+        ### TRANSFORMATION ###
+        content = transform_openai_input_gemini_content(input=input)
 
-        # if "instances" in optional_params:
-        #     request_data["instances"] = optional_params["instances"]
-        # elif isinstance(input, list):
-        #     request_data["instances"] = input
-        # else:
-        #     # construct instances
-        #     vertex_request_instance = Instance(**optional_params)
+        request_data: VertexAITextEmbeddingsRequestBody = {
+            "content": content,
+            **optional_params,
+        }
 
-        #     if isinstance(input, str):
-        #         vertex_request_instance["text"] = input
+        headers = {
+            "Content-Type": "application/json; charset=utf-8",
+        }
 
-        #     request_data["instances"] = [vertex_request_instance]
+        ## LOGGING
+        logging_obj.pre_call(
+            input=input,
+            api_key="",
+            additional_args={
+                "complete_input_dict": request_data,
+                "api_base": url,
+                "headers": headers,
+            },
+        )
 
-        # headers = {
-        #     "Content-Type": "application/json; charset=utf-8",
-        #     "Authorization": f"Bearer {auth_header}",
-        # }
+        if aembedding is True:
+            pass
 
-        # ## LOGGING
-        # logging_obj.pre_call(
-        #     input=input,
-        #     api_key="",
-        #     additional_args={
-        #         "complete_input_dict": request_data,
-        #         "api_base": url,
-        #         "headers": headers,
-        #     },
-        # )
+        response = sync_handler.post(
+            url=url,
+            headers=headers,
+            data=json.dumps(request_data),
+        )
 
-        # if aembedding is True:
-        #     pass
+        if response.status_code != 200:
+            raise Exception(f"Error: {response.status_code} {response.text}")
 
-        # response = sync_handler.post(
-        #     url=url,
-        #     headers=headers,
-        #     data=json.dumps(request_data),
-        # )
+        _json_response = response.json()
+        _predictions = VertexAITextEmbeddingsResponseObject(**_json_response)  # type: ignore
 
-        # if response.status_code != 200:
-        #     raise Exception(f"Error: {response.status_code} {response.text}")
+        model_response.data = [
+            Embedding(
+                embedding=_predictions["embedding"]["values"],
+                index=0,
+                object="embedding",
+            )
+        ]
 
-        # _json_response = response.json()
-        # if "predictions" not in _json_response:
-        #     raise litellm.InternalServerError(
-        #         message=f"embedding response does not contain 'predictions', got {_json_response}",
-        #         llm_provider="vertex_ai",
-        #         model=model,
-        #     )
-        # _predictions = _json_response["predictions"]
+        model_response.model = model
 
-        # model_response.data = _predictions
-        # model_response.model = model
+        input_text = get_formatted_prompt(data={"input": input}, call_type="embedding")
+        prompt_tokens = litellm.token_counter(model=model, text=input_text)
+        model_response.usage = litellm.Usage(
+            prompt_tokens=prompt_tokens, total_tokens=prompt_tokens
+        )
 
-        # return model_response
+        return model_response

litellm/llms/vertex_ai_and_google_ai_studio/gemini/embeddings_transformation.py

@@ -3,3 +3,25 @@ Transformation logic from OpenAI /v1/embeddings format to Google AI Studio /embe
 
 Why separate file? Make it easy to see how transformation works
 """
+
+from typing import List
+
+from litellm.types.llms.openai import EmbeddingInput
+from litellm.types.llms.vertex_ai import ContentType, PartType
+
+from ..common_utils import VertexAIError
+
+
+def transform_openai_input_gemini_content(input: EmbeddingInput) -> ContentType:
+    """
+    The content to embed. Only the parts.text fields will be counted.
+    """
+    if isinstance(input, str):
+        return ContentType(parts=[PartType(text=input)])
+    elif isinstance(input, list) and len(input) == 1:
+        return ContentType(parts=[PartType(text=input[0])])
+    else:
+        raise VertexAIError(
+            status_code=422,
+            message="/embedContent only generates a single text embedding vector. File an issue, to add support for /batchEmbedContent - https://github.com/BerriAI/litellm/issues",
+        )

litellm/main.py

@@ -126,6 +126,9 @@ from .llms.vertex_ai_and_google_ai_studio import (
     vertex_ai_anthropic,
     vertex_ai_non_gemini,
 )
+from .llms.vertex_ai_and_google_ai_studio.gemini.embeddings_handler import (
+    GoogleEmbeddings,
+)
 from .llms.vertex_ai_and_google_ai_studio.gemini.vertex_and_google_ai_studio_gemini import (
     VertexLLM,
 )
@@ -172,6 +175,7 @@ triton_chat_completions = TritonChatCompletion()
 bedrock_chat_completion = BedrockLLM()
 bedrock_converse_chat_completion = BedrockConverseLLM()
 vertex_chat_completion = VertexLLM()
+google_embeddings = GoogleEmbeddings()
 vertex_partner_models_chat_completion = VertexAIPartnerModels()
 vertex_text_to_speech = VertexTextToSpeechAPI()
 watsonxai = IBMWatsonXAI()
@@ -3533,7 +3537,7 @@ def embedding(
 
             gemini_api_key = api_key or get_secret("GEMINI_API_KEY") or litellm.api_key
 
-            response = vertex_chat_completion.multimodal_embedding(  # type: ignore
+            response = google_embeddings.text_embeddings(  # type: ignore
                 model=model,
                 input=input,
                 encoding=encoding,

litellm/tests/test_embedding.py

@@ -697,7 +697,8 @@ async def test_gemini_embeddings():
         print(f"response: {response}")
 
         # stubbed endpoint is setup to return this
-        assert response.data[0]["embedding"] == [0.1, 0.2]
+        assert isinstance(response.data[0]["embedding"], list)
+        assert response.usage.prompt_tokens > 0
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 

litellm/types/llms/openai.py

@@ -30,6 +30,7 @@ from openai.types.beta.threads.message import Message as OpenAIMessage
 from openai.types.beta.threads.message_content import MessageContent
 from openai.types.beta.threads.run import Run
 from openai.types.chat import ChatCompletionChunk
+from openai.types.embedding import Embedding as OpenAIEmbedding
 from pydantic import BaseModel, Field
 from typing_extensions import Dict, Required, TypedDict, override
 
@@ -47,6 +48,9 @@ FileTypes = Union[
 ]
 
 
+EmbeddingInput = Union[str, List[str]]
+
+
 class NotGiven:
     """
     A sentinel singleton class used to distinguish omitted keyword arguments

litellm/types/llms/vertex_ai.py

@@ -336,3 +336,29 @@ class VertexMultimodalEmbeddingRequest(TypedDict, total=False):
 class VertexAICachedContentResponseObject(TypedDict):
     name: str
     model: str
+
+
+class TaskTypeEnum(Enum):
+    TASK_TYPE_UNSPECIFIED = "TASK_TYPE_UNSPECIFIED"
+    RETRIEVAL_QUERY = "RETRIEVAL_QUERY"
+    RETRIEVAL_DOCUMENT = "RETRIEVAL_DOCUMENT"
+    SEMANTIC_SIMILARITY = "SEMANTIC_SIMILARITY"
+    CLASSIFICATION = "CLASSIFICATION"
+    CLUSTERING = "CLUSTERING"
+    QUESTION_ANSWERING = "QUESTION_ANSWERING"
+    FACT_VERIFICATION = "FACT_VERIFICATION"
+
+
+class VertexAITextEmbeddingsRequestBody(TypedDict, total=False):
+    content: Required[ContentType]
+    taskType: TaskTypeEnum
+    title: str
+    outputDimensionality: int
+
+
+class ContentEmbeddings(TypedDict):
+    values: List[int]
+
+
+class VertexAITextEmbeddingsResponseObject(TypedDict):
+    embedding: ContentEmbeddings