feat(vertex_ai_and_google_ai_studio): Support Google AI Studio Embedd…

…ings endpoint

Closes #5385

litellm/llms/vertex_ai_and_google_ai_studio/common_utils.py

@@ -1,4 +1,4 @@
-from typing import Literal
+from typing import Literal, Tuple
 
 import httpx
 
@@ -37,3 +37,62 @@ def get_supports_system_message(
         supports_system_message = False
 
     return supports_system_message
+
+
+from typing import Literal, Optional
+
+all_gemini_url_modes = Literal["chat", "embedding"]
+
+
+def _get_vertex_url(
+    mode: all_gemini_url_modes,
+    model: str,
+    stream: Optional[bool],
+    vertex_project: Optional[str],
+    vertex_location: Optional[str],
+    vertex_api_version: Literal["v1", "v1beta1"],
+) -> Tuple[str, str]:
+    if mode == "chat":
+        ### SET RUNTIME ENDPOINT ###
+        endpoint = "generateContent"
+        if stream is True:
+            endpoint = "streamGenerateContent"
+            url = f"https://{vertex_location}-aiplatform.googleapis.com/{vertex_api_version}/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}?alt=sse"
+        else:
+            url = f"https://{vertex_location}-aiplatform.googleapis.com/{vertex_api_version}/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}"
+
+        # if model is only numeric chars then it's a fine tuned gemini model
+        # model = 4965075652664360960
+        # send to this url: url = f"https://{vertex_location}-aiplatform.googleapis.com/{version}/projects/{vertex_project}/locations/{vertex_location}/endpoints/{model}:{endpoint}"
+        if model.isdigit():
+            # It's a fine-tuned Gemini model
+            url = f"https://{vertex_location}-aiplatform.googleapis.com/{vertex_api_version}/projects/{vertex_project}/locations/{vertex_location}/endpoints/{model}:{endpoint}"
+            if stream is True:
+                url += "?alt=sse"
+
+    return url, endpoint
+
+
+def _get_gemini_url(
+    mode: all_gemini_url_modes,
+    model: str,
+    stream: Optional[bool],
+    gemini_api_key: Optional[str],
+) -> Tuple[str, str]:
+    if mode == "chat":
+        _gemini_model_name = "models/{}".format(model)
+        endpoint = "generateContent"
+        if stream is True:
+            endpoint = "streamGenerateContent"
+            url = "https://generativelanguage.googleapis.com/v1beta/{}:{}?key={}&alt=sse".format(
+                _gemini_model_name, endpoint, gemini_api_key
+            )
+        else:
+            url = (
+                "https://generativelanguage.googleapis.com/v1beta/{}:{}?key={}".format(
+                    _gemini_model_name, endpoint, gemini_api_key
+                )
+            )
+    elif mode == "embedding":
+        pass
+    return url, endpoint

litellm/llms/vertex_ai_and_google_ai_studio/vertex_and_google_ai_studio_gemini.py → litellm/llms/vertex_ai_and_google_ai_studio/gemini/vertex_and_google_ai_studio_gemini.py

@@ -54,10 +54,16 @@
 from litellm.types.utils import GenericStreamingChunk
 from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
 
-from ..base import BaseLLM
-from .common_utils import VertexAIError, get_supports_system_message
-from .context_caching.vertex_ai_context_caching import ContextCachingEndpoints
-from .gemini_transformation import transform_system_message
+from ...base import BaseLLM
+from ..common_utils import (
+    VertexAIError,
+    _get_gemini_url,
+    _get_vertex_url,
+    all_gemini_url_modes,
+    get_supports_system_message,
+)
+from ..context_caching.vertex_ai_context_caching import ContextCachingEndpoints
+from .transformation import transform_system_message
 
 context_caching_endpoints = ContextCachingEndpoints()
 
@@ -309,6 +315,7 @@ def get_supported_openai_params(self):
             "n",
             "stop",
         ]
+
     def _map_function(self, value: List[dict]) -> List[Tools]:
         gtool_func_declarations = []
         googleSearchRetrieval: Optional[dict] = None
@@ -1164,6 +1171,7 @@ def _get_token_and_url(
         custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"],
         api_base: Optional[str],
         should_use_v1beta1_features: Optional[bool] = False,
+        mode: all_gemini_url_modes = "chat",
     ) -> Tuple[Optional[str], str]:
         """
         Internal function. Returns the token and url for the call.
@@ -1174,42 +1182,31 @@ def _get_token_and_url(
             token, url
         """
         if custom_llm_provider == "gemini":
-            _gemini_model_name = "models/{}".format(model)
             auth_header = None
-            endpoint = "generateContent"
-            if stream is True:
-                endpoint = "streamGenerateContent"
-                url = "https://generativelanguage.googleapis.com/v1beta/{}:{}?key={}&alt=sse".format(
-                    _gemini_model_name, endpoint, gemini_api_key
-                )
-            else:
-                url = "https://generativelanguage.googleapis.com/v1beta/{}:{}?key={}".format(
-                    _gemini_model_name, endpoint, gemini_api_key
-                )
+            url, endpoint = _get_gemini_url(
+                mode=mode,
+                model=model,
+                stream=stream,
+                gemini_api_key=gemini_api_key,
+            )
         else:
             auth_header, vertex_project = self._ensure_access_token(
                 credentials=vertex_credentials, project_id=vertex_project
             )
             vertex_location = self.get_vertex_region(vertex_region=vertex_location)
 
             ### SET RUNTIME ENDPOINT ###
-            version = "v1beta1" if should_use_v1beta1_features is True else "v1"
-            endpoint = "generateContent"
-            litellm.utils.print_verbose("vertex_project - {}".format(vertex_project))
-            if stream is True:
-                endpoint = "streamGenerateContent"
-                url = f"https://{vertex_location}-aiplatform.googleapis.com/{version}/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}?alt=sse"
-            else:
-                url = f"https://{vertex_location}-aiplatform.googleapis.com/{version}/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}"
-
-            # if model is only numeric chars then it's a fine tuned gemini model
-            # model = 4965075652664360960
-            # send to this url: url = f"https://{vertex_location}-aiplatform.googleapis.com/{version}/projects/{vertex_project}/locations/{vertex_location}/endpoints/{model}:{endpoint}"
-            if model.isdigit():
-                # It's a fine-tuned Gemini model
-                url = f"https://{vertex_location}-aiplatform.googleapis.com/{version}/projects/{vertex_project}/locations/{vertex_location}/endpoints/{model}:{endpoint}"
-                if stream is True:
-                    url += "?alt=sse"
+            version: Literal["v1beta1", "v1"] = (
+                "v1beta1" if should_use_v1beta1_features is True else "v1"
+            )
+            url, endpoint = _get_vertex_url(
+                mode=mode,
+                model=model,
+                stream=stream,
+                vertex_project=vertex_project,
+                vertex_location=vertex_location,
+                vertex_api_version=version,
+            )
 
         if (
             api_base is not None
@@ -1793,8 +1790,10 @@ def multimodal_embedding(
         input: Union[list, str],
         print_verbose,
         model_response: litellm.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,
@@ -1804,6 +1803,17 @@ def multimodal_embedding(
         timeout=300,
         client=None,
     ):
+        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,
+        )
 
         if client is None:
             _params = {}

litellm/main.py

@@ -126,12 +126,12 @@
     vertex_ai_anthropic,
     vertex_ai_non_gemini,
 )
+from .llms.vertex_ai_and_google_ai_studio.gemini.vertex_and_google_ai_studio_gemini import (
+    VertexLLM,
+)
 from .llms.vertex_ai_and_google_ai_studio.vertex_ai_partner_models.main import (
     VertexAIPartnerModels,
 )
-from .llms.vertex_ai_and_google_ai_studio.vertex_and_google_ai_studio_gemini import (
-    VertexLLM,
-)
 from .llms.watsonx import IBMWatsonXAI
 from .types.llms.openai import HttpxBinaryResponseContent
 from .types.utils import (
@@ -3568,6 +3568,7 @@ def embedding(
                     vertex_credentials=vertex_credentials,
                     aembedding=aembedding,
                     print_verbose=print_verbose,
+                    custom_llm_provider="vertex_ai",
                 )
             else:
                 response = vertex_ai_non_gemini.embedding(

litellm/tests/test_amazing_vertex_completion.py

@@ -28,7 +28,7 @@
     completion_cost,
     embedding,
 )
-from litellm.llms.vertex_ai_and_google_ai_studio.vertex_and_google_ai_studio_gemini import (
+from litellm.llms.vertex_ai_and_google_ai_studio.gemini.vertex_and_google_ai_studio_gemini import (
     _gemini_convert_messages_with_history,
 )
 from litellm.tests.test_streaming import streaming_format_tests
@@ -2065,7 +2065,7 @@ def test_prompt_factory_nested():
 
 
 def test_get_token_url():
-    from litellm.llms.vertex_ai_and_google_ai_studio.vertex_and_google_ai_studio_gemini import (
+    from litellm.llms.vertex_ai_and_google_ai_studio.gemini.vertex_and_google_ai_studio_gemini import (
         VertexLLM,
     )
 
@@ -2087,7 +2087,7 @@ def test_get_token_url():
         vertex_credentials=vertex_credentials,
         gemini_api_key="",
         custom_llm_provider="vertex_ai_beta",
-        should_use_v1beta1_features=should_use_v1beta1_features,
+        should_use_vertex_v1beta1_features=should_use_v1beta1_features,
         api_base=None,
         model="",
         stream=False,
@@ -2107,7 +2107,7 @@ def test_get_token_url():
         vertex_credentials=vertex_credentials,
         gemini_api_key="",
         custom_llm_provider="vertex_ai_beta",
-        should_use_v1beta1_features=should_use_v1beta1_features,
+        should_use_vertex_v1beta1_features=should_use_v1beta1_features,
         api_base=None,
         model="",
         stream=False,