test

# What does this PR do?


## Test Plan
# What does this PR do?


## Test Plan
# What does this PR do?


## Test Plan
Completes the refactoring started in previous commit by:

1. **Fix library client** (critical): Add logic to detect Pydantic model parameters
   and construct them properly from request bodies. The key fix is to NOT exclude
   any params when converting the body for Pydantic models - we need all fields
   to pass to the Pydantic constructor.

   Before: _convert_body excluded all params, leaving body empty for Pydantic construction
   After: Check for Pydantic params first, skip exclusion, construct model with full body

2. **Update remaining providers** to use new Pydantic-based signatures:
   - litellm_openai_mixin: Extract extra fields via __pydantic_extra__
   - databricks: Use TYPE_CHECKING import for params type
   - llama_openai_compat: Use TYPE_CHECKING import for params type
   - sentence_transformers: Update method signatures to use params

3. **Update unit tests** to use new Pydantic signature:
   - test_openai_mixin.py: Use OpenAIChatCompletionRequestParams

This fixes test failures where the library client was trying to construct
Pydantic models with empty dictionaries.
The previous fix had a bug: it called _convert_body() which only keeps fields
that match function parameter names. For Pydantic methods with signature:
  openai_chat_completion(params: OpenAIChatCompletionRequestParams)

The signature only has 'params', but the body has 'model', 'messages', etc.
So _convert_body() returned an empty dict.

Fix: Skip _convert_body() entirely for Pydantic params. Use the raw body
directly to construct the Pydantic model (after stripping NOT_GIVENs).

This properly fixes the ValidationError where required fields were missing.
The streaming code path (_call_streaming) had the same issue as non-streaming:
it called _convert_body() which returned empty dict for Pydantic params.

Applied the same fix as commit 7476c0ae:
- Detect Pydantic model parameters before body conversion
- Skip _convert_body() for Pydantic params
- Construct Pydantic model directly from raw body (after stripping NOT_GIVENs)

This fixes streaming endpoints like openai_chat_completion with stream=True.
The streaming code path (_call_streaming) had the same issue as non-streaming:
it called _convert_body() which returned empty dict for Pydantic params.

Applied the same fix as commit 7476c0ae:
- Detect Pydantic model parameters before body conversion
- Skip _convert_body() for Pydantic params
- Construct Pydantic model directly from raw body (after stripping NOT_GIVENs)

This fixes streaming endpoints like openai_chat_completion with stream=True.

llama_stack/providers/inline/batches/reference/batches.py

@@ -22,6 +22,8 @@ from llama_stack.apis.files import Files, OpenAIFilePurpose
 from llama_stack.apis.inference import (
     Inference,
     OpenAIAssistantMessageParam,
+    OpenAIChatCompletionRequestParams,
+    OpenAICompletionRequestParams,
     OpenAIDeveloperMessageParam,
     OpenAIMessageParam,
     OpenAISystemMessageParam,
@@ -601,7 +603,8 @@ class ReferenceBatchesImpl(Batches):
             # TODO(SECURITY): review body for security issues
             if request.url == "/v1/chat/completions":
                 request.body["messages"] = [convert_to_openai_message_param(msg) for msg in request.body["messages"]]
-                chat_response = await self.inference_api.openai_chat_completion(**request.body)
+                params = OpenAIChatCompletionRequestParams(**request.body)
+                chat_response = await self.inference_api.openai_chat_completion(params)
 
                 # this is for mypy, we don't allow streaming so we'll get the right type
                 assert hasattr(chat_response, "model_dump_json"), "Chat response must have model_dump_json method"
@@ -615,7 +618,8 @@ class ReferenceBatchesImpl(Batches):
                     },
                 }
             else:  # /v1/completions
-                completion_response = await self.inference_api.openai_completion(**request.body)
+                params = OpenAICompletionRequestParams(**request.body)
+                completion_response = await self.inference_api.openai_completion(params)
 
                 # this is for mypy, we don't allow streaming so we'll get the right type
                 assert hasattr(completion_response, "model_dump_json"), (