(fix) unable to pass input_type parameter to Voyage AI embedding mode (#7276)

* VoyageEmbeddingConfig

* fix voyage logic to get params

* add voyage embedding transformation

* add get_provider_embedding_config

* use BaseEmbeddingConfig

* voyage clean up

* use llm http handler for embedding transformations

* test_voyage_ai_embedding_extra_params

* add voyage async

* test_voyage_ai_embedding_extra_params

* add async for llm http handler

* update BaseLLMEmbeddingTest

* test_voyage_ai_embedding_extra_params

* fix linting

* fix get_provider_embedding_config

* fix anthropic text test

* update location of base/chat/transformation

* fix import path

* fix IBMWatsonXAIConfig

litellm/llms/base_llm/embedding/transformation.py

@@ -0,0 +1,94 @@
+import types
+from abc import ABC, abstractmethod
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    AsyncIterator,
+    Callable,
+    Dict,
+    Iterator,
+    List,
+    Optional,
+    TypedDict,
+    Union,
+)
+
+import httpx
+
+from litellm.llms.base_llm.chat.transformation import BaseConfig
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import EmbeddingResponse, ModelResponse
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+    LiteLLMLoggingObj = Any
+
+
+class BaseEmbeddingConfig(BaseConfig, ABC):
+
+    @abstractmethod
+    def transform_embedding_request(
+        self,
+        model: str,
+        input: Union[str, List[str], List[float], List[List[float]]],
+        optional_params: dict,
+        headers: dict,
+    ) -> dict:
+        return {}
+
+    @abstractmethod
+    def transform_embedding_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: EmbeddingResponse,
+        logging_obj: LiteLLMLoggingObj,
+        api_key: Optional[str] = None,
+        request_data: dict = {},
+        optional_params: dict = {},
+        litellm_params: dict = {},
+    ) -> EmbeddingResponse:
+        return model_response
+
+    def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+        """
+        OPTIONAL
+
+        Get the complete url for the request
+
+        Some providers need `model` in `api_base`
+        """
+        return api_base or ""
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        raise NotImplementedError(
+            "EmbeddingConfig does not need a request transformation for chat models"
+        )
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        request_data: dict,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ModelResponse:
+        raise NotImplementedError(
+            "EmbeddingConfig does not need a response transformation for chat models"
+        )