feat(embeddings_handler.py): support async gemini embeddings

litellm/llms/vertex_ai_and_google_ai_studio/gemini/embeddings_handler.py

@@ -9,7 +9,8 @@ import httpx
 
 import litellm
 from litellm import EmbeddingResponse
-from litellm.llms.custom_httpx.http_handler import HTTPHandler
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.types.llms.openai import EmbeddingInput
 from litellm.types.llms.vertex_ai import (
     VertexAITextEmbeddingsRequestBody,
     VertexAITextEmbeddingsResponseObject,
@@ -17,7 +18,10 @@ from litellm.types.llms.vertex_ai import (
 from litellm.types.utils import Embedding
 from litellm.utils import get_formatted_prompt
 
-from .embeddings_transformation import transform_openai_input_gemini_content
+from .embeddings_transformation import (
+    process_response,
+    transform_openai_input_gemini_content,
+)
 from .vertex_and_google_ai_studio_gemini import VertexLLM
 
 
@@ -94,7 +98,16 @@ class GoogleEmbeddings(VertexLLM):
         )
 
         if aembedding is True:
-            pass
+            return self.async_text_embeddings(  # type: ignore
+                model=model,
+                api_base=api_base,
+                url=url,
+                data=request_data,
+                model_response=model_response,
+                timeout=timeout,
+                headers=headers,
+                input=input,
+            )
 
         response = sync_handler.post(
             url=url,
@@ -108,20 +121,53 @@ class GoogleEmbeddings(VertexLLM):
         _json_response = response.json()
         _predictions = VertexAITextEmbeddingsResponseObject(**_json_response)  # type: ignore
 
-        model_response.data = [
+        return process_response(
-            Embedding(
+            model=model,
-                embedding=_predictions["embedding"]["values"],
+            model_response=model_response,
-                index=0,
+            _predictions=_predictions,
-                object="embedding",
+            input=input,
-            )
-        ]
-
-        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
+    async def async_text_embeddings(
+        self,
+        model: str,
+        api_base: Optional[str],
+        url: str,
+        data: VertexAITextEmbeddingsRequestBody,
+        model_response: EmbeddingResponse,
+        input: EmbeddingInput,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        headers={},
+        client: Optional[AsyncHTTPHandler] = None,
+    ) -> EmbeddingResponse:
+        if client is None:
+            _params = {}
+            if timeout is not None:
+                if isinstance(timeout, float) or isinstance(timeout, int):
+                    _httpx_timeout = httpx.Timeout(timeout)
+                    _params["timeout"] = _httpx_timeout
+            else:
+                _params["timeout"] = httpx.Timeout(timeout=600.0, connect=5.0)
+
+            async_handler: AsyncHTTPHandler = AsyncHTTPHandler(**_params)  # type: ignore
+        else:
+            async_handler = client  # type: ignore
+
+        response = await async_handler.post(
+            url=url,
+            headers=headers,
+            data=json.dumps(data),
+        )
+
+        if response.status_code != 200:
+            raise Exception(f"Error: {response.status_code} {response.text}")
+
+        _json_response = response.json()
+        _predictions = VertexAITextEmbeddingsResponseObject(**_json_response)  # type: ignore
+
+        return process_response(
+            model=model,
+            model_response=model_response,
+            _predictions=_predictions,
+            input=input,
+        )

litellm/llms/vertex_ai_and_google_ai_studio/gemini/embeddings_transformation.py

@@ -6,8 +6,15 @@ Why separate file? Make it easy to see how transformation works
 
 from typing import List
 
+from litellm import EmbeddingResponse
 from litellm.types.llms.openai import EmbeddingInput
-from litellm.types.llms.vertex_ai import ContentType, PartType
+from litellm.types.llms.vertex_ai import (
+    ContentType,
+    PartType,
+    VertexAITextEmbeddingsResponseObject,
+)
+from litellm.types.utils import Embedding, Usage
+from litellm.utils import get_formatted_prompt, token_counter
 
 from ..common_utils import VertexAIError
 
@@ -25,3 +32,28 @@ def transform_openai_input_gemini_content(input: EmbeddingInput) -> ContentType:
             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",
         )
+
+
+def process_response(
+    input: EmbeddingInput,
+    model_response: EmbeddingResponse,
+    model: str,
+    _predictions: VertexAITextEmbeddingsResponseObject,
+) -> EmbeddingResponse:
+    model_response.data = [
+        Embedding(
+            embedding=_predictions["embedding"]["values"],
+            index=0,
+            object="embedding",
+        )
+    ]
+
+    model_response.model = model
+
+    input_text = get_formatted_prompt(data={"input": input}, call_type="embedding")
+    prompt_tokens = token_counter(model=model, text=input_text)
+    model_response.usage = Usage(
+        prompt_tokens=prompt_tokens, total_tokens=prompt_tokens
+    )
+
+    return model_response

litellm/tests/test_embedding.py

@@ -686,14 +686,21 @@ async def test_triton_embeddings():
         pytest.fail(f"Error occurred: {e}")
 
 
+@pytest.mark.parametrize("sync_mode", [True, False])
 @pytest.mark.asyncio
-async def test_gemini_embeddings():
+async def test_gemini_embeddings(sync_mode):
     try:
         litellm.set_verbose = True
-        response = await litellm.aembedding(
+        if sync_mode:
-            model="gemini/text-embedding-004",
+            response = litellm.embedding(
-            input=["good morning from litellm"],
+                model="gemini/text-embedding-004",
-        )
+                input=["good morning from litellm"],
+            )
+        else:
+            response = await litellm.aembedding(
+                model="gemini/text-embedding-004",
+                input=["good morning from litellm"],
+            )
         print(f"response: {response}")
 
         # stubbed endpoint is setup to return this