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Eric Huang a70fc60485 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.
2025-10-09 13:53:18 -07:00
..
00bf38cb0b6eef2963c49f52798781840456635d0510be615cda65f93cd1cdfb.json fix(tests): ensure test isolation in server mode (#3737) 2025-10-08 12:03:36 -07:00
0d32af949aaf2d039a88b57ea210e8e02517631e290a0e9cdbbcbe99d1863952.json test 2025-10-09 13:53:18 -07:00
0d502b9a5c71c11c5d80f62f54e6878251724d0ae835918721407da89af9195b.json test 2025-10-09 13:53:18 -07:00
0e44d91278f78682708e855e3161c031454d77a011ec80d7e64b3b8969ad00b2.json chore: turn OpenAIMixin into a pydantic.BaseModel (#3671) 2025-10-06 11:33:19 -04:00
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1a0d3109cf92111ed4cb061a857dee0b99fa1e0b27934de1e6c5d29c03026626.json feat(tests): implement test isolation for inference recordings (#3681) 2025-10-04 11:34:18 -07:00
1cc16917850c6479af8690b3d7c9b8464cd6e3b90425d371aa5edacb22bf2901.json test 2025-10-09 13:53:18 -07:00
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1d82e9439ae3.json feat(tests): implement test isolation for inference recordings (#3681) 2025-10-04 11:34:18 -07:00
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1fd05fad28c2771c56ac8462a742f6004a37724e9c44b32bfc7fef5ac44d744c.json fix(tests): ensure test isolation in server mode (#3737) 2025-10-08 12:03:36 -07:00
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4fc6b187aa6b7f051b9ff2fa2e0fff98710f05ab321a04ab003ccdb1a5ba718f.json fix(tests): ensure test isolation in server mode (#3737) 2025-10-08 12:03:36 -07:00
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5cc7605f9950.json feat(tests): implement test isolation for inference recordings (#3681) 2025-10-04 11:34:18 -07:00
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