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chore: Refactor OpenAIChatCompletion's to be loaded from yaml
Future tests can then re-use the content Signed-off-by: Derek Higgins <derekh@redhat.com>
This commit is contained in:
Derek Higgins
Ben Browning
parent
fe5f5e530c
commit
1369b5858e
4 changed files with 114 additions and 63 deletions
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@ -0,0 +1,74 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import os
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import yaml
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from llama_stack.apis.inference.inference import (
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OpenAIAssistantMessageParam,
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OpenAIChatCompletion,
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OpenAIChatCompletionToolCall,
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OpenAIChatCompletionToolCallFunction,
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OpenAIChoice,
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)
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def load_chat_completion_fixture(filename: str) -> OpenAIChatCompletion:
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"""
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Load a YAML fixture file and convert it to an OpenAIChatCompletion object.
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Args:
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filename: Name of the YAML file (without path)
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Returns:
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OpenAIChatCompletion object
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"""
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fixtures_dir = os.path.dirname(os.path.abspath(__file__))
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fixture_path = os.path.join(fixtures_dir, filename)
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with open(fixture_path) as f:
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data = yaml.safe_load(f)
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choices = []
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for choice_data in data.get("choices", []):
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message_data = choice_data.get("message", {})
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# Handle tool calls if present
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tool_calls = None
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if "tool_calls" in message_data:
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tool_calls = []
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for tool_call_data in message_data.get("tool_calls", []):
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function_data = tool_call_data.get("function", {})
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function = OpenAIChatCompletionToolCallFunction(
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name=function_data.get("name"),
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arguments=function_data.get("arguments"),
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)
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tool_call = OpenAIChatCompletionToolCall(
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id=tool_call_data.get("id"),
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type=tool_call_data.get("type"),
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function=function,
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)
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tool_calls.append(tool_call)
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message = OpenAIAssistantMessageParam(
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content=message_data.get("content"),
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tool_calls=tool_calls,
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)
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choice = OpenAIChoice(
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message=message,
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finish_reason=choice_data.get("finish_reason"),
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index=choice_data.get("index", 0),
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)
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choices.append(choice)
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return OpenAIChatCompletion(
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id=data.get("id"),
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choices=choices,
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created=data.get("created"),
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model=data.get("model"),
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)
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@ -0,0 +1,8 @@
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id: chat-completion-123
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choices:
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- message:
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content: "Dublin"
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finish_reason: stop
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index: 0
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created: 1234567890
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model: meta-llama/Llama-3.1-8B-Instruct
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@ -0,0 +1,13 @@
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id: chat-completion-123
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choices:
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- message:
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tool_calls:
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- id: tool_call_123
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type: function
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function:
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name: web_search
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arguments: '{"query":"What is the capital of Ireland?"}'
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finish_reason: stop
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index: 0
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created: 1234567890
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model: meta-llama/Llama-3.1-8B-Instruct
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@ -13,11 +13,6 @@ from llama_stack.apis.agents.openai_responses import (
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OpenAIResponseOutputMessage,
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)
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from llama_stack.apis.inference.inference import (
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OpenAIAssistantMessageParam,
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OpenAIChatCompletion,
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OpenAIChatCompletionToolCall,
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OpenAIChatCompletionToolCallFunction,
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OpenAIChoice,
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OpenAIUserMessageParam,
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)
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from llama_stack.apis.tools.tools import Tool, ToolGroups, ToolInvocationResult, ToolParameter, ToolRuntime
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@ -25,6 +20,7 @@ from llama_stack.providers.inline.agents.meta_reference.openai_responses import
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OpenAIResponsesImpl,
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)
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from llama_stack.providers.utils.kvstore import KVStore
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from tests.unit.providers.agents.meta_reference.fixtures import load_chat_completion_fixture
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@pytest.fixture
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@ -65,21 +61,11 @@ def openai_responses_impl(mock_kvstore, mock_inference_api, mock_tool_groups_api
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async def test_create_openai_response_with_string_input(openai_responses_impl, mock_inference_api):
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"""Test creating an OpenAI response with a simple string input."""
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# Setup
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input_text = "Hello, world!"
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input_text = "What is the capital of Ireland?"
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model = "meta-llama/Llama-3.1-8B-Instruct"
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mock_chat_completion = OpenAIChatCompletion(
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id="chat-completion-123",
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choices=[
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OpenAIChoice(
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message=OpenAIAssistantMessageParam(content="Hello! How can I help you?"),
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finish_reason="stop",
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index=0,
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)
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],
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created=1234567890,
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model=model,
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)
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# Load the chat completion fixture
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mock_chat_completion = load_chat_completion_fixture("simple_chat_completion.yaml")
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mock_inference_api.openai_chat_completion.return_value = mock_chat_completion
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# Execute
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@ -92,7 +78,7 @@ async def test_create_openai_response_with_string_input(openai_responses_impl, m
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# Verify
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mock_inference_api.openai_chat_completion.assert_called_once_with(
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model=model,
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messages=[OpenAIUserMessageParam(role="user", content="Hello, world!", name=None)],
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messages=[OpenAIUserMessageParam(role="user", content="What is the capital of Ireland?", name=None)],
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tools=None,
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stream=False,
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temperature=0.1,
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@ -101,54 +87,24 @@ async def test_create_openai_response_with_string_input(openai_responses_impl, m
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assert result.model == model
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assert len(result.output) == 1
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assert isinstance(result.output[0], OpenAIResponseOutputMessage)
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assert result.output[0].content[0].text == "Hello! How can I help you?"
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assert result.output[0].content[0].text == "Dublin"
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@pytest.mark.asyncio
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async def test_create_openai_response_with_string_input_with_tools(openai_responses_impl, mock_inference_api):
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"""Test creating an OpenAI response with a simple string input and tools."""
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# Setup
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input_text = "What was the score of todays game?"
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input_text = "What is the capital of Ireland?"
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model = "meta-llama/Llama-3.1-8B-Instruct"
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mock_chat_completions = [
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OpenAIChatCompletion(
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id="chat-completion-123",
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choices=[
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OpenAIChoice(
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message=OpenAIAssistantMessageParam(
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tool_calls=[
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OpenAIChatCompletionToolCall(
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id="tool_call_123",
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type="function",
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function=OpenAIChatCompletionToolCallFunction(
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name="web_search", arguments='{"query":"What was the score of todays game?"}'
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),
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)
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],
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),
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finish_reason="stop",
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index=0,
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)
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],
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created=1234567890,
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model=model,
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),
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OpenAIChatCompletion(
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id="chat-completion-123",
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choices=[
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OpenAIChoice(
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message=OpenAIAssistantMessageParam(content="The score of todays game was 10-12"),
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finish_reason="stop",
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index=0,
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)
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],
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created=1234567890,
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model=model,
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),
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]
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# Load the chat completion fixtures
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tool_call_completion = load_chat_completion_fixture("tool_call_completion.yaml")
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tool_response_completion = load_chat_completion_fixture("simple_chat_completion.yaml")
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mock_inference_api.openai_chat_completion.side_effect = mock_chat_completions
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mock_inference_api.openai_chat_completion.side_effect = [
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tool_call_completion,
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tool_response_completion,
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]
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openai_responses_impl.tool_groups_api.get_tool.return_value = Tool(
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identifier="web_search",
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@ -163,7 +119,7 @@ async def test_create_openai_response_with_string_input_with_tools(openai_respon
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openai_responses_impl.tool_runtime_api.invoke_tool.return_value = ToolInvocationResult(
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status="completed",
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content="The score of todays game was 10-12",
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content="Dublin",
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)
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# Execute
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@ -180,18 +136,18 @@ async def test_create_openai_response_with_string_input_with_tools(openai_respon
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# Verify
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first_call = mock_inference_api.openai_chat_completion.call_args_list[0]
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assert first_call.kwargs["messages"][0].content == "What was the score of todays game?"
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assert first_call.kwargs["messages"][0].content == "What is the capital of Ireland?"
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assert first_call.kwargs["tools"] is not None
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assert first_call.kwargs["temperature"] == 0.1
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second_call = mock_inference_api.openai_chat_completion.call_args_list[1]
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assert second_call.kwargs["messages"][-1].content == "The score of todays game was 10-12"
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assert second_call.kwargs["messages"][-1].content == "Dublin"
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assert second_call.kwargs["temperature"] == 0.1
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openai_responses_impl.tool_groups_api.get_tool.assert_called_once_with("web_search")
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openai_responses_impl.tool_runtime_api.invoke_tool.assert_called_once_with(
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tool_name="web_search",
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kwargs={"query": "What was the score of todays game?"},
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kwargs={"query": "What is the capital of Ireland?"},
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)
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openai_responses_impl.persistence_store.set.assert_called_once()
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@ -199,4 +155,4 @@ async def test_create_openai_response_with_string_input_with_tools(openai_respon
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# Check that we got the content from our mocked tool execution result
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assert len(result.output) >= 1
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assert isinstance(result.output[1], OpenAIResponseOutputMessage)
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assert result.output[1].content[0].text == "The score of todays game was 10-12"
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assert result.output[1].content[0].text == "Dublin"
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