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refactor(responses): move stuff into some utils and add unit tests (#3158)

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# What does this PR do?
Refactors the OpenAI response conversion utilities by moving helper functions from `openai_responses.py` to `utils.py`. Adds unit tests.
This commit is contained in:
ashwinb 2025-08-15 00:05:35 +00:00
parent 47d5af703c
commit 9324e902f1
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3 changed files with 435 additions and 120 deletions
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126
llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py
View file

@ -19,9 +19,6 @@ from llama_stack.apis.agents.openai_responses import (
MCPListToolsTool,
OpenAIDeleteResponseObject,
OpenAIResponseInput,
OpenAIResponseInputFunctionToolCallOutput,
OpenAIResponseInputMessageContent,
OpenAIResponseInputMessageContentImage,
OpenAIResponseInputMessageContentText,
OpenAIResponseInputTool,
OpenAIResponseInputToolMCP,
@ -29,9 +26,6 @@ from llama_stack.apis.agents.openai_responses import (
OpenAIResponseObject,
OpenAIResponseObjectStream,
OpenAIResponseOutput,
OpenAIResponseOutputMessageContent,
OpenAIResponseOutputMessageContentOutputText,
OpenAIResponseOutputMessageFunctionToolCall,
OpenAIResponseOutputMessageMCPListTools,
OpenAIResponseText,
OpenAIResponseTextFormat,
@ -39,23 +33,7 @@ from llama_stack.apis.agents.openai_responses import (
)
from llama_stack.apis.inference import (
Inference,
OpenAIAssistantMessageParam,
OpenAIChatCompletionContentPartImageParam,
OpenAIChatCompletionContentPartParam,
OpenAIChatCompletionContentPartTextParam,
OpenAIChatCompletionToolCall,
OpenAIChatCompletionToolCallFunction,
OpenAIDeveloperMessageParam,
OpenAIImageURL,
OpenAIJSONSchema,
OpenAIMessageParam,
OpenAIResponseFormatJSONObject,
OpenAIResponseFormatJSONSchema,
OpenAIResponseFormatParam,
OpenAIResponseFormatText,
OpenAISystemMessageParam,
OpenAIToolMessageParam,
OpenAIUserMessageParam,
)
from llama_stack.apis.tools import Tool, ToolGroups, ToolRuntime
from llama_stack.apis.vector_io import VectorIO
@ -69,106 +47,14 @@ from llama_stack.providers.utils.responses.responses_store import ResponsesStore
from .streaming import StreamingResponseOrchestrator
from .tool_executor import ToolExecutor
from .types import ChatCompletionContext
from .utils import (
convert_response_input_to_chat_messages,
convert_response_text_to_chat_response_format,
)
logger = get_logger(name=__name__, category="responses")
async def _convert_response_content_to_chat_content(
content: (str | list[OpenAIResponseInputMessageContent] | list[OpenAIResponseOutputMessageContent]),
) -> str | list[OpenAIChatCompletionContentPartParam]:
"""
Convert the content parts from an OpenAI Response API request into OpenAI Chat Completion content parts.
The content schemas of each API look similar, but are not exactly the same.
"""
if isinstance(content, str):
return content
converted_parts = []
for content_part in content:
if isinstance(content_part, OpenAIResponseInputMessageContentText):
converted_parts.append(OpenAIChatCompletionContentPartTextParam(text=content_part.text))
elif isinstance(content_part, OpenAIResponseOutputMessageContentOutputText):
converted_parts.append(OpenAIChatCompletionContentPartTextParam(text=content_part.text))
elif isinstance(content_part, OpenAIResponseInputMessageContentImage):
if content_part.image_url:
image_url = OpenAIImageURL(url=content_part.image_url, detail=content_part.detail)
converted_parts.append(OpenAIChatCompletionContentPartImageParam(image_url=image_url))
elif isinstance(content_part, str):
converted_parts.append(OpenAIChatCompletionContentPartTextParam(text=content_part))
else:
raise ValueError(
f"Llama Stack OpenAI Responses does not yet support content type '{type(content_part)}' in this context"
)
return converted_parts
async def _convert_response_input_to_chat_messages(
input: str | list[OpenAIResponseInput],
) -> list[OpenAIMessageParam]:
"""
Convert the input from an OpenAI Response API request into OpenAI Chat Completion messages.
"""
messages: list[OpenAIMessageParam] = []
if isinstance(input, list):
for input_item in input:
if isinstance(input_item, OpenAIResponseInputFunctionToolCallOutput):
messages.append(
OpenAIToolMessageParam(
content=input_item.output,
tool_call_id=input_item.call_id,
)
)
elif isinstance(input_item, OpenAIResponseOutputMessageFunctionToolCall):
tool_call = OpenAIChatCompletionToolCall(
index=0,
id=input_item.call_id,
function=OpenAIChatCompletionToolCallFunction(
name=input_item.name,
arguments=input_item.arguments,
),
)
messages.append(OpenAIAssistantMessageParam(tool_calls=[tool_call]))
else:
content = await _convert_response_content_to_chat_content(input_item.content)
message_type = await _get_message_type_by_role(input_item.role)
if message_type is None:
raise ValueError(
f"Llama Stack OpenAI Responses does not yet support message role '{input_item.role}' in this context"
)
messages.append(message_type(content=content))
else:
messages.append(OpenAIUserMessageParam(content=input))
return messages
async def _convert_response_text_to_chat_response_format(
text: OpenAIResponseText,
) -> OpenAIResponseFormatParam:
"""
Convert an OpenAI Response text parameter into an OpenAI Chat Completion response format.
"""
if not text.format or text.format["type"] == "text":
return OpenAIResponseFormatText(type="text")
if text.format["type"] == "json_object":
return OpenAIResponseFormatJSONObject()
if text.format["type"] == "json_schema":
return OpenAIResponseFormatJSONSchema(
json_schema=OpenAIJSONSchema(name=text.format["name"], schema=text.format["schema"])
)
raise ValueError(f"Unsupported text format: {text.format}")
async def _get_message_type_by_role(role: str):
role_to_type = {
"user": OpenAIUserMessageParam,
"system": OpenAISystemMessageParam,
"assistant": OpenAIAssistantMessageParam,
"developer": OpenAIDeveloperMessageParam,
}
return role_to_type.get(role)
class OpenAIResponsePreviousResponseWithInputItems(BaseModel):
input_items: ListOpenAIResponseInputItem
response: OpenAIResponseObject
@ -350,11 +236,11 @@ class OpenAIResponsesImpl:
) -> AsyncIterator[OpenAIResponseObjectStream]:
# Input preprocessing
input = await self._prepend_previous_response(input, previous_response_id)
messages = await _convert_response_input_to_chat_messages(input)
messages = await convert_response_input_to_chat_messages(input)
await self._prepend_instructions(messages, instructions)
# Structured outputs
response_format = await _convert_response_text_to_chat_response_format(text)
response_format = await convert_response_text_to_chat_response_format(text)
# Tool setup, TODO: refactor this slightly since this can also yield events
chat_tools, mcp_tool_to_server, mcp_list_message = (

119
llama_stack/providers/inline/agents/meta_reference/responses/utils.py
View file

@ -7,14 +7,37 @@
import uuid
from llama_stack.apis.agents.openai_responses import (
OpenAIResponseInput,
OpenAIResponseInputFunctionToolCallOutput,
OpenAIResponseInputMessageContent,
OpenAIResponseInputMessageContentImage,
OpenAIResponseInputMessageContentText,
OpenAIResponseInputTool,
OpenAIResponseMessage,
OpenAIResponseOutputMessageContent,
OpenAIResponseOutputMessageContentOutputText,
OpenAIResponseOutputMessageFunctionToolCall,
OpenAIResponseText,
)
from llama_stack.apis.inference import (
OpenAIAssistantMessageParam,
OpenAIChatCompletionContentPartImageParam,
OpenAIChatCompletionContentPartParam,
OpenAIChatCompletionContentPartTextParam,
OpenAIChatCompletionToolCall,
OpenAIChatCompletionToolCallFunction,
OpenAIChoice,
OpenAIDeveloperMessageParam,
OpenAIImageURL,
OpenAIJSONSchema,
OpenAIMessageParam,
OpenAIResponseFormatJSONObject,
OpenAIResponseFormatJSONSchema,
OpenAIResponseFormatParam,
OpenAIResponseFormatText,
OpenAISystemMessageParam,
OpenAIToolMessageParam,
OpenAIUserMessageParam,
)
@ -38,6 +61,102 @@ async def convert_chat_choice_to_response_message(choice: OpenAIChoice) -> OpenA
)
async def convert_response_content_to_chat_content(
content: (str | list[OpenAIResponseInputMessageContent] | list[OpenAIResponseOutputMessageContent]),
) -> str | list[OpenAIChatCompletionContentPartParam]:
"""
Convert the content parts from an OpenAI Response API request into OpenAI Chat Completion content parts.
The content schemas of each API look similar, but are not exactly the same.
"""
if isinstance(content, str):
return content
converted_parts = []
for content_part in content:
if isinstance(content_part, OpenAIResponseInputMessageContentText):
converted_parts.append(OpenAIChatCompletionContentPartTextParam(text=content_part.text))
elif isinstance(content_part, OpenAIResponseOutputMessageContentOutputText):
converted_parts.append(OpenAIChatCompletionContentPartTextParam(text=content_part.text))
elif isinstance(content_part, OpenAIResponseInputMessageContentImage):
if content_part.image_url:
image_url = OpenAIImageURL(url=content_part.image_url, detail=content_part.detail)
converted_parts.append(OpenAIChatCompletionContentPartImageParam(image_url=image_url))
elif isinstance(content_part, str):
converted_parts.append(OpenAIChatCompletionContentPartTextParam(text=content_part))
else:
raise ValueError(
f"Llama Stack OpenAI Responses does not yet support content type '{type(content_part)}' in this context"
)
return converted_parts
async def convert_response_input_to_chat_messages(
input: str | list[OpenAIResponseInput],
) -> list[OpenAIMessageParam]:
"""
Convert the input from an OpenAI Response API request into OpenAI Chat Completion messages.
"""
messages: list[OpenAIMessageParam] = []
if isinstance(input, list):
for input_item in input:
if isinstance(input_item, OpenAIResponseInputFunctionToolCallOutput):
messages.append(
OpenAIToolMessageParam(
content=input_item.output,
tool_call_id=input_item.call_id,
)
)
elif isinstance(input_item, OpenAIResponseOutputMessageFunctionToolCall):
tool_call = OpenAIChatCompletionToolCall(
index=0,
id=input_item.call_id,
function=OpenAIChatCompletionToolCallFunction(
name=input_item.name,
arguments=input_item.arguments,
),
)
messages.append(OpenAIAssistantMessageParam(tool_calls=[tool_call]))
else:
content = await convert_response_content_to_chat_content(input_item.content)
message_type = await get_message_type_by_role(input_item.role)
if message_type is None:
raise ValueError(
f"Llama Stack OpenAI Responses does not yet support message role '{input_item.role}' in this context"
)
messages.append(message_type(content=content))
else:
messages.append(OpenAIUserMessageParam(content=input))
return messages
async def convert_response_text_to_chat_response_format(
text: OpenAIResponseText,
) -> OpenAIResponseFormatParam:
"""
Convert an OpenAI Response text parameter into an OpenAI Chat Completion response format.
"""
if not text.format or text.format["type"] == "text":
return OpenAIResponseFormatText(type="text")
if text.format["type"] == "json_object":
return OpenAIResponseFormatJSONObject()
if text.format["type"] == "json_schema":
return OpenAIResponseFormatJSONSchema(
json_schema=OpenAIJSONSchema(name=text.format["name"], schema=text.format["schema"])
)
raise ValueError(f"Unsupported text format: {text.format}")
async def get_message_type_by_role(role: str):
role_to_type = {
"user": OpenAIUserMessageParam,
"system": OpenAISystemMessageParam,
"assistant": OpenAIAssistantMessageParam,
"developer": OpenAIDeveloperMessageParam,
}
return role_to_type.get(role)
def is_function_tool_call(
tool_call: OpenAIChatCompletionToolCall,
tools: list[OpenAIResponseInputTool],