feat(batch_embed_content_transformation.py): support google ai studio /batchEmbedContent endpoint

Allows for multiple strings to be given for embedding

litellm/llms/vertex_ai_and_google_ai_studio/common_utils.py

@@ -41,7 +41,7 @@ def get_supports_system_message(
 
 from typing import Literal, Optional
 
-all_gemini_url_modes = Literal["chat", "embedding"]
+all_gemini_url_modes = Literal["chat", "embedding", "batch_embedding"]
 
 
 def _get_vertex_url(
@@ -101,4 +101,10 @@ def _get_gemini_url(
         url = "https://generativelanguage.googleapis.com/v1beta/{}:{}?key={}".format(
             _gemini_model_name, endpoint, gemini_api_key
         )
+    elif mode == "batch_embedding":
+        endpoint = "batchEmbedContents"
+        url = "https://generativelanguage.googleapis.com/v1beta/{}:{}?key={}".format(
+            _gemini_model_name, endpoint, gemini_api_key
+        )
+
     return url, endpoint

litellm/llms/vertex_ai_and_google_ai_studio/embeddings/batch_embed_content_handler.py

@@ -0,0 +1,167 @@
+"""
+Google AI Studio /batchEmbedContents Embeddings Endpoint
+"""
+
+import json
+from typing import List, Literal, Optional, Union
+
+import httpx
+
+from litellm import EmbeddingResponse
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.types.llms.openai import EmbeddingInput
+from litellm.types.llms.vertex_ai import (
+    VertexAIBatchEmbeddingsRequestBody,
+    VertexAIBatchEmbeddingsResponseObject,
+)
+
+from ..gemini.vertex_and_google_ai_studio_gemini import VertexLLM
+from .batch_embed_content_transformation import (
+    process_response,
+    transform_openai_input_gemini_content,
+)
+
+
+class GoogleBatchEmbeddings(VertexLLM):
+    def batch_embeddings(
+        self,
+        model: str,
+        input: List[str],
+        print_verbose,
+        model_response: EmbeddingResponse,
+        custom_llm_provider: Literal["gemini", "vertex_ai"],
+        optional_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        logging_obj=None,
+        encoding=None,
+        vertex_project=None,
+        vertex_location=None,
+        vertex_credentials=None,
+        aembedding=False,
+        timeout=300,
+        client=None,
+    ) -> EmbeddingResponse:
+
+        auth_header, url = self._get_token_and_url(
+            model=model,
+            gemini_api_key=api_key,
+            vertex_project=vertex_project,
+            vertex_location=vertex_location,
+            vertex_credentials=vertex_credentials,
+            stream=None,
+            custom_llm_provider=custom_llm_provider,
+            api_base=api_base,
+            should_use_v1beta1_features=False,
+            mode="batch_embedding",
+        )
+
+        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)
+
+            sync_handler: HTTPHandler = HTTPHandler(**_params)  # type: ignore
+        else:
+            sync_handler = client  # type: ignore
+
+        optional_params = optional_params or {}
+
+        ### TRANSFORMATION ###
+        request_data = transform_openai_input_gemini_content(
+            input=input, model=model, optional_params=optional_params
+        )
+
+        headers = {
+            "Content-Type": "application/json; charset=utf-8",
+        }
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input=input,
+            api_key="",
+            additional_args={
+                "complete_input_dict": request_data,
+                "api_base": url,
+                "headers": headers,
+            },
+        )
+
+        if aembedding is True:
+            return self.async_batch_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,
+            headers=headers,
+            data=json.dumps(request_data),
+        )
+
+        if response.status_code != 200:
+            raise Exception(f"Error: {response.status_code} {response.text}")
+
+        _json_response = response.json()
+        _predictions = VertexAIBatchEmbeddingsResponseObject(**_json_response)  # type: ignore
+
+        return process_response(
+            model=model,
+            model_response=model_response,
+            _predictions=_predictions,
+            input=input,
+        )
+
+    async def async_batch_embeddings(
+        self,
+        model: str,
+        api_base: Optional[str],
+        url: str,
+        data: VertexAIBatchEmbeddingsRequestBody,
+        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 = VertexAIBatchEmbeddingsResponseObject(**_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/embeddings/batch_embed_content_transformation.py

@@ -0,0 +1,68 @@
+"""
+Transformation logic from OpenAI /v1/embeddings format to Google AI Studio /batchEmbedContents format. 
+
+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,
+    EmbedContentRequest,
+    PartType,
+    VertexAIBatchEmbeddingsRequestBody,
+    VertexAIBatchEmbeddingsResponseObject,
+)
+from litellm.types.utils import Embedding, Usage
+from litellm.utils import get_formatted_prompt, token_counter
+
+from ..common_utils import VertexAIError
+
+
+def transform_openai_input_gemini_content(
+    input: List[str], model: str, optional_params: dict
+) -> VertexAIBatchEmbeddingsRequestBody:
+    """
+    The content to embed. Only the parts.text fields will be counted.
+    """
+    gemini_model_name = "models/{}".format(model)
+    requests: List[EmbedContentRequest] = []
+    for i in input:
+        request = EmbedContentRequest(
+            model=gemini_model_name,
+            content=ContentType(parts=[PartType(text=i)]),
+            **optional_params
+        )
+        requests.append(request)
+
+    return VertexAIBatchEmbeddingsRequestBody(requests=requests)
+
+
+def process_response(
+    input: EmbeddingInput,
+    model_response: EmbeddingResponse,
+    model: str,
+    _predictions: VertexAIBatchEmbeddingsResponseObject,
+) -> EmbeddingResponse:
+
+    openai_embeddings: List[Embedding] = []
+    for embedding in _predictions["embeddings"]:
+        openai_embedding = Embedding(
+            embedding=embedding["values"],
+            index=0,
+            object="embedding",
+        )
+        openai_embeddings.append(openai_embedding)
+
+    model_response.data = openai_embeddings
+    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/llms/vertex_ai_and_google_ai_studio/embeddings/embed_content_handler.py

@@ -1,5 +1,5 @@
 """
-Google AI Studio Embeddings Endpoint
+Google AI Studio /embedContent Embeddings Endpoint
 """
 
 import json
@@ -7,7 +7,6 @@ from typing import Literal, Optional, Union
 
 import httpx
 
-import litellm
 from litellm import EmbeddingResponse
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
 from litellm.types.llms.openai import EmbeddingInput
@@ -15,21 +14,19 @@ 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 (
+from ..gemini.vertex_and_google_ai_studio_gemini import VertexLLM
+from .embed_content_transformation import (
     process_response,
     transform_openai_input_gemini_content,
 )
-from .vertex_and_google_ai_studio_gemini import VertexLLM
 
 
 class GoogleEmbeddings(VertexLLM):
     def text_embeddings(
         self,
         model: str,
-        input: Union[list, str],
+        input: str,
         print_verbose,
         model_response: EmbeddingResponse,
         custom_llm_provider: Literal["gemini", "vertex_ai"],

litellm/llms/vertex_ai_and_google_ai_studio/embeddings/embed_content_transformation.py

@@ -4,8 +4,6 @@ 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 import EmbeddingResponse
 from litellm.types.llms.openai import EmbeddingInput
 from litellm.types.llms.vertex_ai import (
@@ -19,19 +17,11 @@ from litellm.utils import get_formatted_prompt, token_counter
 from ..common_utils import VertexAIError
 
 
-def transform_openai_input_gemini_content(input: EmbeddingInput) -> ContentType:
+def transform_openai_input_gemini_content(input: str) -> 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",
-        )
 
 
 def process_response(

litellm/main.py

@@ -126,7 +126,10 @@ 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 (
+from .llms.vertex_ai_and_google_ai_studio.embeddings.batch_embed_content_handler import (
+    GoogleBatchEmbeddings,
+)
+from .llms.vertex_ai_and_google_ai_studio.embeddings.embed_content_handler import (
     GoogleEmbeddings,
 )
 from .llms.vertex_ai_and_google_ai_studio.gemini.vertex_and_google_ai_studio_gemini import (
@@ -176,6 +179,7 @@ bedrock_chat_completion = BedrockLLM()
 bedrock_converse_chat_completion = BedrockConverseLLM()
 vertex_chat_completion = VertexLLM()
 google_embeddings = GoogleEmbeddings()
+google_batch_embeddings = GoogleBatchEmbeddings()
 vertex_partner_models_chat_completion = VertexAIPartnerModels()
 vertex_text_to_speech = VertexTextToSpeechAPI()
 watsonxai = IBMWatsonXAI()
@@ -3537,6 +3541,7 @@ def embedding(
 
             gemini_api_key = api_key or get_secret("GEMINI_API_KEY") or litellm.api_key
 
+            if isinstance(input, str):
                 response = google_embeddings.text_embeddings(  # type: ignore
                     model=model,
                     input=input,
@@ -3552,6 +3557,22 @@ def embedding(
                     custom_llm_provider="gemini",
                     api_key=gemini_api_key,
                 )
+            else:
+                response = google_batch_embeddings.batch_embeddings(  # type: ignore
+                    model=model,
+                    input=input,
+                    encoding=encoding,
+                    logging_obj=logging,
+                    optional_params=optional_params,
+                    model_response=EmbeddingResponse(),
+                    vertex_project=None,
+                    vertex_location=None,
+                    vertex_credentials=None,
+                    aembedding=aembedding,
+                    print_verbose=print_verbose,
+                    custom_llm_provider="gemini",
+                    api_key=gemini_api_key,
+                )
 
         elif custom_llm_provider == "vertex_ai":
             vertex_ai_project = (

litellm/tests/test_embedding.py

@@ -687,19 +687,22 @@ async def test_triton_embeddings():
 
 
 @pytest.mark.parametrize("sync_mode", [True, False])
+@pytest.mark.parametrize(
+    "input", ["good morning from litellm", ["good morning from litellm"]]  #
+)
 @pytest.mark.asyncio
-async def test_gemini_embeddings(sync_mode):
+async def test_gemini_embeddings(sync_mode, input):
     try:
         litellm.set_verbose = True
         if sync_mode:
             response = litellm.embedding(
                 model="gemini/text-embedding-004",
-                input=["good morning from litellm"],
+                input=input,
             )
         else:
             response = await litellm.aembedding(
                 model="gemini/text-embedding-004",
-                input=["good morning from litellm"],
+                input=input,
             )
         print(f"response: {response}")
 

litellm/types/llms/vertex_ai.py

@@ -362,3 +362,15 @@ class ContentEmbeddings(TypedDict):
 
 class VertexAITextEmbeddingsResponseObject(TypedDict):
     embedding: ContentEmbeddings
+
+
+class EmbedContentRequest(VertexAITextEmbeddingsRequestBody):
+    model: Required[str]
+
+
+class VertexAIBatchEmbeddingsRequestBody(TypedDict, total=False):
+    requests: List[EmbedContentRequest]
+
+
+class VertexAIBatchEmbeddingsResponseObject(TypedDict):
+    embeddings: List[ContentEmbeddings]