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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 2025-10-09 13:53:17 -07:00
parent 9e9a827fcd
commit a70fc60485
295 changed files with 51966 additions and 3051 deletions
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50
llama_stack/providers/remote/inference/bedrock/bedrock.py
View file

@ -13,6 +13,8 @@ from botocore.client import BaseClient
from llama_stack.apis.inference import (
ChatCompletionRequest,
Inference,
OpenAIChatCompletionRequestParams,
OpenAICompletionRequestParams,
OpenAIEmbeddingsResponse,
)
from llama_stack.apis.inference.inference import (
@ -135,56 +137,12 @@ class BedrockInferenceAdapter(
async def openai_completion(
self,
# Standard OpenAI completion parameters
model: str,
prompt: str | list[str] | list[int] | list[list[int]],
best_of: int | None = None,
echo: bool | None = None,
frequency_penalty: float | None = None,
logit_bias: dict[str, float] | None = None,
logprobs: bool | None = None,
max_tokens: int | None = None,
n: int | None = None,
presence_penalty: float | None = None,
seed: int | None = None,
stop: str | list[str] | None = None,
stream: bool | None = None,
stream_options: dict[str, Any] | None = None,
temperature: float | None = None,
top_p: float | None = None,
user: str | None = None,
# vLLM-specific parameters
guided_choice: list[str] | None = None,
prompt_logprobs: int | None = None,
# for fill-in-the-middle type completion
suffix: str | None = None,
params: OpenAICompletionRequestParams,
) -> OpenAICompletion:
raise NotImplementedError("OpenAI completion not supported by the Bedrock provider")
async def openai_chat_completion(
self,
model: str,
messages: list[OpenAIMessageParam],
frequency_penalty: float | None = None,
function_call: str | dict[str, Any] | None = None,
functions: list[dict[str, Any]] | None = None,
logit_bias: dict[str, float] | None = None,
logprobs: bool | None = None,
max_completion_tokens: int | None = None,
max_tokens: int | None = None,
n: int | None = None,
parallel_tool_calls: bool | None = None,
presence_penalty: float | None = None,
response_format: OpenAIResponseFormatParam | None = None,
seed: int | None = None,
stop: str | list[str] | None = None,
stream: bool | None = None,
stream_options: dict[str, Any] | None = None,
temperature: float | None = None,
tool_choice: str | dict[str, Any] | None = None,
tools: list[dict[str, Any]] | None = None,
top_logprobs: int | None = None,
top_p: float | None = None,
user: str | None = None,
params: OpenAIChatCompletionRequestParams,
) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
raise NotImplementedError("OpenAI chat completion not supported by the Bedrock provider")

26
llama_stack/providers/remote/inference/databricks/databricks.py
View file

@ -5,11 +5,14 @@
# the root directory of this source tree.
from collections.abc import Iterable
from typing import Any
from typing import TYPE_CHECKING, Any
from databricks.sdk import WorkspaceClient
from llama_stack.apis.inference import OpenAICompletion
if TYPE_CHECKING:
from llama_stack.apis.inference import OpenAICompletionRequestParams
from llama_stack.log import get_logger
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
@ -43,25 +46,6 @@ class DatabricksInferenceAdapter(OpenAIMixin):
async def openai_completion(
self,
model: str,
prompt: str | list[str] | list[int] | list[list[int]],
best_of: int | None = None,
echo: bool | None = None,
frequency_penalty: float | None = None,
logit_bias: dict[str, float] | None = None,
logprobs: bool | None = None,
max_tokens: int | None = None,
n: int | None = None,
presence_penalty: float | None = None,
seed: int | None = None,
stop: str | list[str] | None = None,
stream: bool | None = None,
stream_options: dict[str, Any] | None = None,
temperature: float | None = None,
top_p: float | None = None,
user: str | None = None,
guided_choice: list[str] | None = None,
prompt_logprobs: int | None = None,
suffix: str | None = None,
params: "OpenAICompletionRequestParams",
) -> OpenAICompletion:
raise NotImplementedError()

26
llama_stack/providers/remote/inference/llama_openai_compat/llama.py
View file

@ -3,9 +3,12 @@
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any
from typing import TYPE_CHECKING
from llama_stack.apis.inference.inference import OpenAICompletion, OpenAIEmbeddingsResponse
if TYPE_CHECKING:
from llama_stack.apis.inference import OpenAICompletionRequestParams
from llama_stack.log import get_logger
from llama_stack.providers.remote.inference.llama_openai_compat.config import LlamaCompatConfig
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
@ -34,26 +37,7 @@ class LlamaCompatInferenceAdapter(OpenAIMixin):
async def openai_completion(
self,
model: str,
prompt: str | list[str] | list[int] | list[list[int]],
best_of: int | None = None,
echo: bool | None = None,
frequency_penalty: float | None = None,
logit_bias: dict[str, float] | None = None,
logprobs: bool | None = None,
max_tokens: int | None = None,
n: int | None = None,
presence_penalty: float | None = None,
seed: int | None = None,
stop: str | list[str] | None = None,
stream: bool | None = None,
stream_options: dict[str, Any] | None = None,
temperature: float | None = None,
top_p: float | None = None,
user: str | None = None,
guided_choice: list[str] | None = None,
prompt_logprobs: int | None = None,
suffix: str | None = None,
params: "OpenAICompletionRequestParams",
) -> OpenAICompletion:
raise NotImplementedError()

114
llama_stack/providers/remote/inference/passthrough/passthrough.py
View file

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

2
llama_stack/providers/remote/inference/runpod/runpod.py
View file

@ -57,6 +57,7 @@ class RunpodInferenceAdapter(OpenAIMixin):
top_logprobs: int | None = None,
top_p: float | None = None,
user: str | None = None,
**kwargs: Any,
):
"""Override to add RunPod-specific stream_options requirement."""
if stream and not stream_options:
@ -86,4 +87,5 @@ class RunpodInferenceAdapter(OpenAIMixin):
top_logprobs=top_logprobs,
top_p=top_p,
user=user,
**kwargs,
)

2
llama_stack/providers/remote/inference/vllm/vllm.py
View file

@ -102,6 +102,7 @@ class VLLMInferenceAdapter(OpenAIMixin):
top_logprobs: int | None = None,
top_p: float | None = None,
user: str | None = None,
**kwargs: Any,
) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
max_tokens = max_tokens or self.config.max_tokens
@ -136,4 +137,5 @@ class VLLMInferenceAdapter(OpenAIMixin):
top_logprobs=top_logprobs,
top_p=top_p,
user=user,
**kwargs,
)