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# 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.
This commit is contained in:
Eric Huang
parent
26fd5dbd34
commit
a93130e323
295 changed files with 51966 additions and 3051 deletions
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@ -13,15 +13,14 @@ from llama_stack.apis.inference import (
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Inference,
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OpenAIChatCompletion,
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OpenAIChatCompletionChunk,
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OpenAIChatCompletionRequestParams,
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OpenAICompletion,
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OpenAICompletionRequestParams,
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OpenAIEmbeddingsResponse,
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OpenAIMessageParam,
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OpenAIResponseFormatParam,
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)
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from llama_stack.apis.models import Model
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from llama_stack.core.library_client import convert_pydantic_to_json_value
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from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
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from llama_stack.providers.utils.inference.openai_compat import prepare_openai_completion_params
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from .config import PassthroughImplConfig
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@ -80,110 +79,33 @@ class PassthroughInferenceAdapter(Inference):
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async def openai_completion(
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self,
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model: str,
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prompt: str | list[str] | list[int] | list[list[int]],
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best_of: int | None = None,
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echo: bool | None = None,
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frequency_penalty: float | None = None,
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logit_bias: dict[str, float] | None = None,
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logprobs: bool | None = None,
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max_tokens: int | None = None,
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n: int | None = None,
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presence_penalty: float | None = None,
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seed: int | None = None,
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stop: str | list[str] | None = None,
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stream: bool | None = None,
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stream_options: dict[str, Any] | None = None,
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temperature: float | None = None,
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top_p: float | None = None,
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user: str | None = None,
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guided_choice: list[str] | None = None,
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prompt_logprobs: int | None = None,
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suffix: str | None = None,
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params: OpenAICompletionRequestParams,
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) -> OpenAICompletion:
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client = self._get_client()
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model_obj = await self.model_store.get_model(model)
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model_obj = await self.model_store.get_model(params.model)
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params = await prepare_openai_completion_params(
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model=model_obj.provider_resource_id,
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prompt=prompt,
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best_of=best_of,
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echo=echo,
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frequency_penalty=frequency_penalty,
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logit_bias=logit_bias,
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logprobs=logprobs,
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max_tokens=max_tokens,
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n=n,
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presence_penalty=presence_penalty,
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seed=seed,
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stop=stop,
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stream=stream,
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stream_options=stream_options,
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temperature=temperature,
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top_p=top_p,
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user=user,
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guided_choice=guided_choice,
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prompt_logprobs=prompt_logprobs,
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)
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# Update model with provider resource ID
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params.model = model_obj.provider_resource_id
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return await client.inference.openai_completion(**params)
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# Convert Pydantic model to dict, including extra fields
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request_params = params.model_dump(exclude_none=True)
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return await client.inference.openai_completion(**request_params)
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async def openai_chat_completion(
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self,
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model: str,
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messages: list[OpenAIMessageParam],
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frequency_penalty: float | None = None,
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function_call: str | dict[str, Any] | None = None,
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functions: list[dict[str, Any]] | None = None,
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logit_bias: dict[str, float] | None = None,
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logprobs: bool | None = None,
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max_completion_tokens: int | None = None,
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max_tokens: int | None = None,
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n: int | None = None,
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parallel_tool_calls: bool | None = None,
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presence_penalty: float | None = None,
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response_format: OpenAIResponseFormatParam | None = None,
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seed: int | None = None,
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stop: str | list[str] | None = None,
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stream: bool | None = None,
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stream_options: dict[str, Any] | None = None,
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temperature: float | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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tools: list[dict[str, Any]] | None = None,
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top_logprobs: int | None = None,
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top_p: float | None = None,
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user: str | None = None,
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params: OpenAIChatCompletionRequestParams,
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) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
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client = self._get_client()
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model_obj = await self.model_store.get_model(model)
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model_obj = await self.model_store.get_model(params.model)
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params = await prepare_openai_completion_params(
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model=model_obj.provider_resource_id,
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messages=messages,
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frequency_penalty=frequency_penalty,
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function_call=function_call,
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functions=functions,
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logit_bias=logit_bias,
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logprobs=logprobs,
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max_completion_tokens=max_completion_tokens,
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max_tokens=max_tokens,
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n=n,
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parallel_tool_calls=parallel_tool_calls,
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presence_penalty=presence_penalty,
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response_format=response_format,
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seed=seed,
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stop=stop,
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stream=stream,
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stream_options=stream_options,
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temperature=temperature,
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tool_choice=tool_choice,
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tools=tools,
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top_logprobs=top_logprobs,
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top_p=top_p,
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user=user,
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)
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# Update model with provider resource ID
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params.model = model_obj.provider_resource_id
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return await client.inference.openai_chat_completion(**params)
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# Convert Pydantic model to dict, including extra fields
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request_params = params.model_dump(exclude_none=True)
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return await client.inference.openai_chat_completion(**request_params)
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def cast_value_to_json_dict(self, request_params: dict[str, Any]) -> dict[str, Any]:
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json_params = {}
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