feat(api)!: support extra_body to embeddings and vector_stores APIs (#3794)

Applies the same pattern from
https://github.com/llamastack/llama-stack/pull/3777 to embeddings and
vector_stores.create() endpoints.

This should _not_ be a breaking change since (a) our tests were already
using the `extra_body` parameter when passing in to the backend (b) but
the backend probably wasn't extracting the parameters correctly. This PR
will fix that.

Updated APIs: `openai_embeddings(), openai_create_vector_store(),
openai_create_vector_store_file_batch()`

llama_stack/providers/utils/inference/embedding_mixin.py

@@ -17,6 +17,7 @@ if TYPE_CHECKING:
 from llama_stack.apis.inference import (
     ModelStore,
     OpenAIEmbeddingData,
+    OpenAIEmbeddingsRequestWithExtraBody,
     OpenAIEmbeddingsResponse,
     OpenAIEmbeddingUsage,
 )
@@ -32,26 +33,22 @@ class SentenceTransformerEmbeddingMixin:
 
     async def openai_embeddings(
         self,
-        model: str,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
+        params: OpenAIEmbeddingsRequestWithExtraBody,
     ) -> OpenAIEmbeddingsResponse:
         # Convert input to list format if it's a single string
-        input_list = [input] if isinstance(input, str) else input
+        input_list = [params.input] if isinstance(params.input, str) else params.input
         if not input_list:
             raise ValueError("Empty list not supported")
 
         # Get the model and generate embeddings
-        model_obj = await self.model_store.get_model(model)
+        model_obj = await self.model_store.get_model(params.model)
         embedding_model = await self._load_sentence_transformer_model(model_obj.provider_resource_id)
         embeddings = await asyncio.to_thread(embedding_model.encode, input_list, show_progress_bar=False)
 
         # Convert embeddings to the requested format
         data = []
         for i, embedding in enumerate(embeddings):
-            if encoding_format == "base64":
+            if params.encoding_format == "base64":
                 # Convert float array to base64 string
                 float_bytes = struct.pack(f"{len(embedding)}f", *embedding)
                 embedding_value = base64.b64encode(float_bytes).decode("ascii")
@@ -70,7 +67,7 @@ class SentenceTransformerEmbeddingMixin:
         usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1)
         return OpenAIEmbeddingsResponse(
             data=data,
-            model=model,
+            model=params.model,
             usage=usage,
         )