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/eval/meta_reference/eval.py

@@ -12,7 +12,14 @@ from llama_stack.apis.agents import Agents, StepType
 from llama_stack.apis.benchmarks import Benchmark
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Datasets
-from llama_stack.apis.inference import Inference, OpenAISystemMessageParam, OpenAIUserMessageParam, UserMessage
+from llama_stack.apis.inference import (
+    Inference,
+    OpenAIChatCompletionRequestParams,
+    OpenAICompletionRequestParams,
+    OpenAISystemMessageParam,
+    OpenAIUserMessageParam,
+    UserMessage,
+)
 from llama_stack.apis.scoring import Scoring
 from llama_stack.providers.datatypes import BenchmarksProtocolPrivate
 from llama_stack.providers.inline.agents.meta_reference.agent_instance import (
@@ -168,11 +175,12 @@ class MetaReferenceEvalImpl(
                     sampling_params["stop"] = candidate.sampling_params.stop
 
                 input_content = json.loads(x[ColumnName.completion_input.value])
-                response = await self.inference_api.openai_completion(
+                params = OpenAICompletionRequestParams(
                     model=candidate.model,
                     prompt=input_content,
                     **sampling_params,
                 )
+                response = await self.inference_api.openai_completion(params)
                 generations.append({ColumnName.generated_answer.value: response.choices[0].text})
             elif ColumnName.chat_completion_input.value in x:
                 chat_completion_input_json = json.loads(x[ColumnName.chat_completion_input.value])
@@ -187,11 +195,12 @@ class MetaReferenceEvalImpl(
                 messages += [OpenAISystemMessageParam(**x) for x in chat_completion_input_json if x["role"] == "system"]
 
                 messages += input_messages
-                response = await self.inference_api.openai_chat_completion(
+                params = OpenAIChatCompletionRequestParams(
                     model=candidate.model,
                     messages=messages,
                     **sampling_params,
                 )
+                response = await self.inference_api.openai_chat_completion(params)
                 generations.append({ColumnName.generated_answer.value: response.choices[0].message.content})
             else:
                 raise ValueError("Invalid input row")