(sap) Align embedding request transformation with current API

docs/my-website/docs/providers/sap.md

@@ -595,7 +595,7 @@ For more information about all available translation configurations, see the [do
 The data masking module serves to anonymize or pseudonymize personally identifiable information from the input for selected entities.
 
 ```python showLineNumbers title="Data Masking Example"
-from litellm import completion
+from litellm import completion, embedding
 masking_config = {
             'providers':
                 [
@@ -620,9 +620,19 @@ response = completion(model="sap/gpt-4o",
                       placeholder_values={"cv": mock_cv},
                       masking=masking_config)
 print(response.choices[0].message.content)
+
+###Data masking module also available for embedding 
+response = embedding(model="sap/text-embedding-3-small",
+                      input=mock_cv,
+                      masking=masking_config)
+print(response.data[0])
 ```
 For more information about all available data masking configurations, see the [documentation](https://help.sap.com/docs/sap-ai-core/generative-ai/enhancing-model-consumption-with-data-masking-66ad6f469afc4c2cbaa91a27a33f7b21?locale=en-US)
 
+
+
+
+
 #### Content Filtering
 The content filtering module allows you to filter input and output based on content safety criteria. 
 
@@ -680,7 +690,7 @@ except Exception as e:
 ```
 For more information about all available content filtering configurations, see the [documentation](https://help.sap.com/docs/sap-ai-core/generative-ai/content-filtering?locale=en-US)
 
-#### List of moduls configuration for fallback
+#### List of modules configuration for fallback
 SAP GEN AI Hub supports a fallback mechanism for handling errors. This mechanism allows you to specify a list of fallback modules to use in case of errors. The fallback modules should contain all parameters that are required for configuring the request.
 
 Required parameters:
@@ -700,8 +710,6 @@ Optional parameters:
 ```python showLineNumbers title="Fallback Example"
 from litellm import completion
 
-from litellm import completion
-
 translation_config = {
     'input':
         {'type': 'sap_document_translation',

litellm/llms/sap/embed/transformation.py

@@ -5,6 +5,7 @@ Translates from OpenAI's `/v1/embeddings` to IBM's `/text/embeddings` route.
 from typing import Optional, List, Dict, Literal, Union
 from pydantic import BaseModel, Field
 from functools import cached_property
+from litellm.llms.sap.chat.models import MaskingModuleConfig
 
 import httpx
 
@@ -48,24 +49,31 @@ class EmbeddingModel(BaseModel):
     name: str
     version: str = "latest"
     params: dict = Field(default_factory=dict, validation_alias="parameters")
+    timeout: Optional[int] = Field(default=600, ge=1, le=600)
+    max_retries: Optional[int] = Field(default=2, ge=0, le=5)
 
+class EmbeddingsModelConfig(BaseModel):
+    model: EmbeddingModel
 
 class EmbeddingsModules(BaseModel):
-    embeddings: EmbeddingModel
+    embeddings: EmbeddingsModelConfig
+    masking: Optional[MaskingModuleConfig] = None
 
 
 class EmbeddingInput(BaseModel):
     text: Union[str, List[str]]
     type: Literal["text", "document", "query"] = "text"
 
+class EmbeddingComfig(BaseModel):
+    modules: EmbeddingsModules
 
 class EmbeddingRequest(BaseModel):
-    config: EmbeddingsModules
+    config: EmbeddingComfig
     input: EmbeddingInput
 
 
 def validate_dict(data: dict, model) -> dict:
-    return model(**data).model_dump()
+    return model(**data).model_dump(exclude_none=True, by_alias=True)
 
 
 class GenAIHubEmbeddingConfig(BaseEmbeddingConfig):
@@ -153,14 +161,20 @@ class GenAIHubEmbeddingConfig(BaseEmbeddingConfig):
         model_dict["version"] = optional_params.get("version", "latest")
         model_dict["params"] = optional_params.get("parameters", {})
         input_dict = {"text": input}
+        if optional_params.get("type"):
+            input_dict["type"] = optional_params.get("type")
+        masking = {"masking": optional_params.get("masking")} if optional_params.get("masking") else {}
         body = {
             "config": {
                 "modules": {
-                    "embeddings": {"model": validate_dict(model_dict, EmbeddingModel)}
+                    "embeddings": {"model": model_dict},
+                    **masking
                 }
             },
-            "input": validate_dict(input_dict, EmbeddingInput),
+            "input": input_dict,
         }
+        body = validate_dict(body, EmbeddingRequest)
+
         return body
 
     def transform_embedding_response(

tests/test_litellm/llms/sap/embed/test_sap_embed_transformation.py

@@ -21,7 +21,9 @@ def test_basic_config_transform(fake_token_creator, fake_deployment_url):
                     'model': {
                         'name': 'text-embedding-3-small',
                         'version': 'latest',
-                        'params': {}
+                        'params': {},
+                        'timeout': 600,
+                        'max_retries': 2
                     }
                 }
             }