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https://github.com/meta-llama/llama-stack.git
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f1b103e6c8
# What does this PR do? When converting OpenAI message content for the "system" and "assistant" roles to Llama Stack inference APIs (used for some providers when dealing with Llama models via OpenAI API requests to get proper prompt / tool handling), we were not properly converting any non-string content. I discovered this while running the new Responses AI verification suite against the Fireworks provider, but instead of fixing it as part of some ongoing work there split this out into a separate PR. This fixes that, by using the `openai_content_to_content` helper we used elsewhere to ensure content parts were mapped properly. ## Test Plan I added a couple of new tests to `test_openai_compat` to reproduce this issue and validate its fix. I ran those as below: ``` python -m pytest -s -v tests/unit/providers/utils/inference/test_openai_compat.py ``` Signed-off-by: Ben Browning <bbrownin@redhat.com>
116 lines
4.1 KiB
Python
116 lines
4.1 KiB
Python
# 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 pytest
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from llama_stack.apis.common.content_types import TextContentItem
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from llama_stack.apis.inference.inference import (
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CompletionMessage,
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OpenAIAssistantMessageParam,
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OpenAIChatCompletionContentPartTextParam,
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OpenAISystemMessageParam,
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OpenAIUserMessageParam,
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SystemMessage,
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UserMessage,
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)
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from llama_stack.models.llama.datatypes import BuiltinTool, StopReason, ToolCall
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from llama_stack.providers.utils.inference.openai_compat import (
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convert_message_to_openai_dict,
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openai_messages_to_messages,
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)
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@pytest.mark.asyncio
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async def test_convert_message_to_openai_dict():
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message = UserMessage(content=[TextContentItem(text="Hello, world!")], role="user")
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assert await convert_message_to_openai_dict(message) == {
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"role": "user",
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"content": [{"type": "text", "text": "Hello, world!"}],
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}
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# Test convert_message_to_openai_dict with a tool call
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@pytest.mark.asyncio
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async def test_convert_message_to_openai_dict_with_tool_call():
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message = CompletionMessage(
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content="",
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tool_calls=[
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ToolCall(call_id="123", tool_name="test_tool", arguments_json='{"foo": "bar"}', arguments={"foo": "bar"})
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],
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stop_reason=StopReason.end_of_turn,
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)
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openai_dict = await convert_message_to_openai_dict(message)
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assert openai_dict == {
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"role": "assistant",
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"content": [{"type": "text", "text": ""}],
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"tool_calls": [
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{"id": "123", "type": "function", "function": {"name": "test_tool", "arguments": '{"foo": "bar"}'}}
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],
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}
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@pytest.mark.asyncio
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async def test_convert_message_to_openai_dict_with_builtin_tool_call():
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message = CompletionMessage(
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content="",
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tool_calls=[
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ToolCall(
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call_id="123",
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tool_name=BuiltinTool.brave_search,
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arguments_json='{"foo": "bar"}',
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arguments={"foo": "bar"},
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)
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],
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stop_reason=StopReason.end_of_turn,
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)
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openai_dict = await convert_message_to_openai_dict(message)
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assert openai_dict == {
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"role": "assistant",
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"content": [{"type": "text", "text": ""}],
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"tool_calls": [
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{"id": "123", "type": "function", "function": {"name": "brave_search", "arguments": '{"foo": "bar"}'}}
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],
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}
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@pytest.mark.asyncio
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async def test_openai_messages_to_messages_with_content_str():
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openai_messages = [
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OpenAISystemMessageParam(content="system message"),
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OpenAIUserMessageParam(content="user message"),
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OpenAIAssistantMessageParam(content="assistant message"),
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]
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llama_messages = openai_messages_to_messages(openai_messages)
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assert len(llama_messages) == 3
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assert isinstance(llama_messages[0], SystemMessage)
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assert isinstance(llama_messages[1], UserMessage)
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assert isinstance(llama_messages[2], CompletionMessage)
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assert llama_messages[0].content == "system message"
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assert llama_messages[1].content == "user message"
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assert llama_messages[2].content == "assistant message"
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@pytest.mark.asyncio
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async def test_openai_messages_to_messages_with_content_list():
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openai_messages = [
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OpenAISystemMessageParam(content=[OpenAIChatCompletionContentPartTextParam(text="system message")]),
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OpenAIUserMessageParam(content=[OpenAIChatCompletionContentPartTextParam(text="user message")]),
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OpenAIAssistantMessageParam(content=[OpenAIChatCompletionContentPartTextParam(text="assistant message")]),
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]
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llama_messages = openai_messages_to_messages(openai_messages)
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assert len(llama_messages) == 3
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assert isinstance(llama_messages[0], SystemMessage)
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assert isinstance(llama_messages[1], UserMessage)
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assert isinstance(llama_messages[2], CompletionMessage)
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assert llama_messages[0].content[0].text == "system message"
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assert llama_messages[1].content[0].text == "user message"
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assert llama_messages[2].content[0].text == "assistant message"
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