phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-10-12 13:57:57 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

refactor, body

Browse source 
# What does this PR do?


## Test Plan
# What does this PR do?


## Test Plan
This commit is contained in:
Eric Huang 2025-10-10 13:43:21 -07:00
parent 32fde8d9a8
commit b22d531600
33 changed files with 584 additions and 875 deletions
Download patch file Download diff file Expand all files Collapse all files

68
tests/unit/providers/agents/meta_reference/test_openai_responses.py
View file

@ -33,6 +33,7 @@ from llama_stack.apis.agents.openai_responses import (
from llama_stack.apis.inference import (
OpenAIAssistantMessageParam,
OpenAIChatCompletionContentPartTextParam,
OpenaiChatCompletionRequest,
OpenAIDeveloperMessageParam,
OpenAIJSONSchema,
OpenAIResponseFormatJSONObject,
@ -161,15 +162,17 @@ async def test_create_openai_response_with_string_input(openai_responses_impl, m
chunks = [chunk async for chunk in result]
mock_inference_api.openai_chat_completion.assert_called_once_with(
model=model,
messages=[OpenAIUserMessageParam(role="user", content="What is the capital of Ireland?", name=None)],
response_format=None,
tools=None,
stream=True,
temperature=0.1,
stream_options={
"include_usage": True,
},
OpenaiChatCompletionRequest(
model=model,
messages=[OpenAIUserMessageParam(role="user", content="What is the capital of Ireland?", name=None)],
response_format=None,
tools=None,
stream=True,
temperature=0.1,
stream_options={
"include_usage": True,
},
)
)
# Should have content part events for text streaming
@ -256,13 +259,15 @@ async def test_create_openai_response_with_string_input_with_tools(openai_respon
# Verify
first_call = mock_inference_api.openai_chat_completion.call_args_list[0]
assert first_call.kwargs["messages"][0].content == "What is the capital of Ireland?"
assert first_call.kwargs["tools"] is not None
assert first_call.kwargs["temperature"] == 0.1
first_params = first_call.args[0]
assert first_params.messages[0].content == "What is the capital of Ireland?"
assert first_params.tools is not None
assert first_params.temperature == 0.1
second_call = mock_inference_api.openai_chat_completion.call_args_list[1]
assert second_call.kwargs["messages"][-1].content == "Dublin"
assert second_call.kwargs["temperature"] == 0.1
second_params = second_call.args[0]
assert second_params.messages[-1].content == "Dublin"
assert second_params.temperature == 0.1
openai_responses_impl.tool_groups_api.get_tool.assert_called_once_with("web_search")
openai_responses_impl.tool_runtime_api.invoke_tool.assert_called_once_with(
@ -348,9 +353,10 @@ async def test_create_openai_response_with_tool_call_type_none(openai_responses_
# Verify inference API was called correctly (after iterating over result)
first_call = mock_inference_api.openai_chat_completion.call_args_list[0]
assert first_call.kwargs["messages"][0].content == input_text
assert first_call.kwargs["tools"] is not None
assert first_call.kwargs["temperature"] == 0.1
first_params = first_call.args[0]
assert first_params.messages[0].content == input_text
assert first_params.tools is not None
assert first_params.temperature == 0.1
# Check response.created event (should have empty output)
assert len(chunks[0].response.output) == 0
@ -394,9 +400,10 @@ async def test_create_openai_response_with_tool_call_function_arguments_none(ope
def assert_common_expectations(chunks) -> None:
first_call = mock_inference_api.openai_chat_completion.call_args_list[0]
assert first_call.kwargs["messages"][0].content == input_text
assert first_call.kwargs["tools"] is not None
assert first_call.kwargs["temperature"] == 0.1
first_params = first_call.args[0]
assert first_params.messages[0].content == input_text
assert first_params.tools is not None
assert first_params.temperature == 0.1
assert len(chunks[0].response.output) == 0
completed_chunk = chunks[-1]
assert completed_chunk.type == "response.completed"
@ -512,7 +519,9 @@ async def test_create_openai_response_with_multiple_messages(openai_responses_im
# Verify the the correct messages were sent to the inference API i.e.
# All of the responses message were convered to the chat completion message objects
inference_messages = mock_inference_api.openai_chat_completion.call_args_list[0].kwargs["messages"]
call_args = mock_inference_api.openai_chat_completion.call_args_list[0]
params = call_args.args[0]
inference_messages = params.messages
for i, m in enumerate(input_messages):
if isinstance(m.content, str):
assert inference_messages[i].content == m.content
@ -680,7 +689,8 @@ async def test_create_openai_response_with_instructions(openai_responses_impl, m
# Verify
mock_inference_api.openai_chat_completion.assert_called_once()
call_args = mock_inference_api.openai_chat_completion.call_args
sent_messages = call_args.kwargs["messages"]
params = call_args.args[0]
sent_messages = params.messages
# Check that instructions were prepended as a system message
assert len(sent_messages) == 2
@ -718,7 +728,8 @@ async def test_create_openai_response_with_instructions_and_multiple_messages(
# Verify
mock_inference_api.openai_chat_completion.assert_called_once()
call_args = mock_inference_api.openai_chat_completion.call_args
sent_messages = call_args.kwargs["messages"]
params = call_args.args[0]
sent_messages = params.messages
# Check that instructions were prepended as a system message
assert len(sent_messages) == 4 # 1 system + 3 input messages
@ -778,7 +789,8 @@ async def test_create_openai_response_with_instructions_and_previous_response(
# Verify
mock_inference_api.openai_chat_completion.assert_called_once()
call_args = mock_inference_api.openai_chat_completion.call_args
sent_messages = call_args.kwargs["messages"]
params = call_args.args[0]
sent_messages = params.messages
# Check that instructions were prepended as a system message
assert len(sent_messages) == 4, sent_messages
@ -1018,7 +1030,8 @@ async def test_reuse_mcp_tool_list(
)
assert len(mock_inference_api.openai_chat_completion.call_args_list) == 2
second_call = mock_inference_api.openai_chat_completion.call_args_list[1]
tools_seen = second_call.kwargs["tools"]
second_params = second_call.args[0]
tools_seen = second_params.tools
assert len(tools_seen) == 1
assert tools_seen[0]["function"]["name"] == "test_tool"
assert tools_seen[0]["function"]["description"] == "a test tool"
@ -1065,8 +1078,9 @@ async def test_create_openai_response_with_text_format(
# Verify
first_call = mock_inference_api.openai_chat_completion.call_args_list[0]
assert first_call.kwargs["messages"][0].content == input_text
assert first_call.kwargs["response_format"] == response_format
first_params = first_call.args[0]
assert first_params.messages[0].content == input_text
assert first_params.response_format == response_format
async def test_create_openai_response_with_invalid_text_format(openai_responses_impl, mock_inference_api):

15
tests/unit/providers/inference/test_remote_vllm.py
View file

@ -13,6 +13,7 @@ import pytest
from llama_stack.apis.inference import (
OpenAIAssistantMessageParam,
OpenAIChatCompletion,
OpenaiChatCompletionRequest,
OpenAIChoice,
ToolChoice,
)
@ -56,13 +57,14 @@ async def test_old_vllm_tool_choice(vllm_inference_adapter):
mock_client_property.return_value = mock_client
# No tools but auto tool choice
await vllm_inference_adapter.openai_chat_completion(
"mock-model",
[],
params = OpenaiChatCompletionRequest(
model="mock-model",
messages=[{"role": "user", "content": "test"}],
stream=False,
tools=None,
tool_choice=ToolChoice.auto.value,
)
await vllm_inference_adapter.openai_chat_completion(params)
mock_client.chat.completions.create.assert_called()
call_args = mock_client.chat.completions.create.call_args
# Ensure tool_choice gets converted to none for older vLLM versions
@ -171,9 +173,12 @@ async def test_openai_chat_completion_is_async(vllm_inference_adapter):
)
async def do_inference():
await vllm_inference_adapter.openai_chat_completion(
"mock-model", messages=["one fish", "two fish"], stream=False
params = OpenaiChatCompletionRequest(
model="mock-model",
messages=[{"role": "user", "content": "one fish two fish"}],
stream=False,
)
await vllm_inference_adapter.openai_chat_completion(params)
with patch.object(VLLMInferenceAdapter, "client", new_callable=PropertyMock) as mock_create_client:
mock_client = MagicMock()

8
tests/unit/providers/utils/inference/test_openai_mixin.py
View file

@ -12,7 +12,7 @@ from unittest.mock import AsyncMock, MagicMock, Mock, PropertyMock, patch
import pytest
from pydantic import BaseModel, Field
from llama_stack.apis.inference import Model, OpenAIUserMessageParam
from llama_stack.apis.inference import Model, OpenaiChatCompletionRequest, OpenAIUserMessageParam
from llama_stack.apis.models import ModelType
from llama_stack.core.request_headers import request_provider_data_context
from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
@ -271,7 +271,8 @@ class TestOpenAIMixinImagePreprocessing:
with patch("llama_stack.providers.utils.inference.openai_mixin.localize_image_content") as mock_localize:
mock_localize.return_value = (b"fake_image_data", "jpeg")
await mixin.openai_chat_completion(model="test-model", messages=[message])
params = OpenaiChatCompletionRequest(model="test-model", messages=[message])
await mixin.openai_chat_completion(params)
mock_localize.assert_called_once_with("http://example.com/image.jpg")
@ -303,7 +304,8 @@ class TestOpenAIMixinImagePreprocessing:
with patch.object(type(mixin), "client", new_callable=PropertyMock, return_value=mock_client):
with patch("llama_stack.providers.utils.inference.openai_mixin.localize_image_content") as mock_localize:
await mixin.openai_chat_completion(model="test-model", messages=[message])
params = OpenaiChatCompletionRequest(model="test-model", messages=[message])
await mixin.openai_chat_completion(params)
mock_localize.assert_not_called()