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fix: Restore previous responses to input list, not messages

Browse source 
This adjusts the restoration of previous responses to prepend them to
the list of Responses API inputs instead of our converted list of Chat
Completion messages. This matches the expected behavior of the
Responses API, and I misinterpreted the nuances here in the initial implementation.

Signed-off-by: Ben Browning <bbrownin@redhat.com>
This commit is contained in:
Ben Browning 2025-05-02 11:08:09 -04:00
parent 5b2e850754
commit b90bb66f28
7 changed files with 428 additions and 364 deletions
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242
docs/_static/llama-stack-spec.html vendored
View file

@ -6466,54 +6466,15 @@
],
"title": "AgentTurnResponseTurnStartPayload"
},
"OpenAIResponseInputMessage": {
"type": "object",
"properties": {
"content": {
"oneOf": [
{
"type": "string"
},
{
"type": "array",
"items": {
"$ref": "#/components/schemas/OpenAIResponseInputMessageContent"
}
}
]
"OpenAIResponseInput": {
"oneOf": [
{
"$ref": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall"
},
"role": {
"oneOf": [
{
"type": "string",
"const": "system"
},
{
"type": "string",
"const": "developer"
},
{
"type": "string",
"const": "user"
},
{
"type": "string",
"const": "assistant"
}
]
},
"type": {
"type": "string",
"const": "message",
"default": "message"
{
"$ref": "#/components/schemas/OpenAIResponseMessage"
}
},
"additionalProperties": false,
"required": [
"content",
"role"
],
"title": "OpenAIResponseInputMessage"
]
},
"OpenAIResponseInputMessageContent": {
"oneOf": [
@ -6614,6 +6575,111 @@
],
"title": "OpenAIResponseInputToolWebSearch"
},
"OpenAIResponseMessage": {
"type": "object",
"properties": {
"content": {
"oneOf": [
{
"type": "string"
},
{
"type": "array",
"items": {
"$ref": "#/components/schemas/OpenAIResponseInputMessageContent"
}
},
{
"type": "array",
"items": {
"$ref": "#/components/schemas/OpenAIResponseOutputMessageContent"
}
}
]
},
"role": {
"oneOf": [
{
"type": "string",
"const": "system"
},
{
"type": "string",
"const": "developer"
},
{
"type": "string",
"const": "user"
},
{
"type": "string",
"const": "assistant"
}
]
},
"type": {
"type": "string",
"const": "message",
"default": "message"
},
"id": {
"type": "string"
},
"status": {
"type": "string"
}
},
"additionalProperties": false,
"required": [
"content",
"role",
"type"
],
"title": "OpenAIResponseMessage",
"description": "Corresponds to the various Message types in the Responses API. They are all under one type because the Responses API gives them all the same \"type\" value, and there is no way to tell them apart in certain scenarios."
},
"OpenAIResponseOutputMessageContent": {
"type": "object",
"properties": {
"text": {
"type": "string"
},
"type": {
"type": "string",
"const": "output_text",
"default": "output_text"
}
},
"additionalProperties": false,
"required": [
"text",
"type"
],
"title": "OpenAIResponseOutputMessageContentOutputText"
},
"OpenAIResponseOutputMessageWebSearchToolCall": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"status": {
"type": "string"
},
"type": {
"type": "string",
"const": "web_search_call",
"default": "web_search_call"
}
},
"additionalProperties": false,
"required": [
"id",
"status",
"type"
],
"title": "OpenAIResponseOutputMessageWebSearchToolCall"
},
"CreateOpenaiResponseRequest": {
"type": "object",
"properties": {
@ -6625,7 +6691,7 @@
{
"type": "array",
"items": {
"$ref": "#/components/schemas/OpenAIResponseInputMessage"
"$ref": "#/components/schemas/OpenAIResponseInput"
}
}
],
@ -6743,7 +6809,7 @@
"OpenAIResponseOutput": {
"oneOf": [
{
"$ref": "#/components/schemas/OpenAIResponseOutputMessage"
"$ref": "#/components/schemas/OpenAIResponseMessage"
},
{
"$ref": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall"
@ -6752,89 +6818,11 @@
"discriminator": {
"propertyName": "type",
"mapping": {
"message": "#/components/schemas/OpenAIResponseOutputMessage",
"message": "#/components/schemas/OpenAIResponseMessage",
"web_search_call": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall"
}
}
},
"OpenAIResponseOutputMessage": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"content": {
"type": "array",
"items": {
"$ref": "#/components/schemas/OpenAIResponseOutputMessageContent"
}
},
"role": {
"type": "string",
"const": "assistant",
"default": "assistant"
},
"status": {
"type": "string"
},
"type": {
"type": "string",
"const": "message",
"default": "message"
}
},
"additionalProperties": false,
"required": [
"id",
"content",
"role",
"status",
"type"
],
"title": "OpenAIResponseOutputMessage"
},
"OpenAIResponseOutputMessageContent": {
"type": "object",
"properties": {
"text": {
"type": "string"
},
"type": {
"type": "string",
"const": "output_text",
"default": "output_text"
}
},
"additionalProperties": false,
"required": [
"text",
"type"
],
"title": "OpenAIResponseOutputMessageContentOutputText"
},
"OpenAIResponseOutputMessageWebSearchToolCall": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"status": {
"type": "string"
},
"type": {
"type": "string",
"const": "web_search_call",
"default": "web_search_call"
}
},
"additionalProperties": false,
"required": [
"id",
"status",
"type"
],
"title": "OpenAIResponseOutputMessageWebSearchToolCall"
},
"OpenAIResponseObjectStream": {
"oneOf": [
{

171
docs/_static/llama-stack-spec.yaml vendored
View file

@ -4534,34 +4534,10 @@ components:
- event_type
- turn_id
title: AgentTurnResponseTurnStartPayload
OpenAIResponseInputMessage:
type: object
properties:
content:
oneOf:
- type: string
- type: array
items:
$ref: '#/components/schemas/OpenAIResponseInputMessageContent'
role:
oneOf:
- type: string
const: system
- type: string
const: developer
- type: string
const: user
- type: string
const: assistant
type:
type: string
const: message
default: message
additionalProperties: false
required:
- content
- role
title: OpenAIResponseInputMessage
OpenAIResponseInput:
oneOf:
- $ref: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall'
- $ref: '#/components/schemas/OpenAIResponseMessage'
OpenAIResponseInputMessageContent:
oneOf:
- $ref: '#/components/schemas/OpenAIResponseInputMessageContentText'
@ -4625,6 +4601,79 @@ components:
required:
- type
title: OpenAIResponseInputToolWebSearch
OpenAIResponseMessage:
type: object
properties:
content:
oneOf:
- type: string
- type: array
items:
$ref: '#/components/schemas/OpenAIResponseInputMessageContent'
- type: array
items:
$ref: '#/components/schemas/OpenAIResponseOutputMessageContent'
role:
oneOf:
- type: string
const: system
- type: string
const: developer
- type: string
const: user
- type: string
const: assistant
type:
type: string
const: message
default: message
id:
type: string
status:
type: string
additionalProperties: false
required:
- content
- role
- type
title: OpenAIResponseMessage
description: >-
Corresponds to the various Message types in the Responses API. They are all
under one type because the Responses API gives them all the same "type" value,
and there is no way to tell them apart in certain scenarios.
OpenAIResponseOutputMessageContent:
type: object
properties:
text:
type: string
type:
type: string
const: output_text
default: output_text
additionalProperties: false
required:
- text
- type
title: >-
OpenAIResponseOutputMessageContentOutputText
"OpenAIResponseOutputMessageWebSearchToolCall":
type: object
properties:
id:
type: string
status:
type: string
type:
type: string
const: web_search_call
default: web_search_call
additionalProperties: false
required:
- id
- status
- type
title: >-
OpenAIResponseOutputMessageWebSearchToolCall
CreateOpenaiResponseRequest:
type: object
properties:
@ -4633,7 +4682,7 @@ components:
- type: string
- type: array
items:
$ref: '#/components/schemas/OpenAIResponseInputMessage'
$ref: '#/components/schemas/OpenAIResponseInput'
description: Input message(s) to create the response.
model:
type: string
@ -4717,73 +4766,13 @@ components:
title: OpenAIResponseObject
OpenAIResponseOutput:
oneOf:
- $ref: '#/components/schemas/OpenAIResponseOutputMessage'
- $ref: '#/components/schemas/OpenAIResponseMessage'
- $ref: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall'
discriminator:
propertyName: type
mapping:
message: '#/components/schemas/OpenAIResponseOutputMessage'
message: '#/components/schemas/OpenAIResponseMessage'
web_search_call: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall'
OpenAIResponseOutputMessage:
type: object
properties:
id:
type: string
content:
type: array
items:
$ref: '#/components/schemas/OpenAIResponseOutputMessageContent'
role:
type: string
const: assistant
default: assistant
status:
type: string
type:
type: string
const: message
default: message
additionalProperties: false
required:
- id
- content
- role
- status
- type
title: OpenAIResponseOutputMessage
OpenAIResponseOutputMessageContent:
type: object
properties:
text:
type: string
type:
type: string
const: output_text
default: output_text
additionalProperties: false
required:
- text
- type
title: >-
OpenAIResponseOutputMessageContentOutputText
"OpenAIResponseOutputMessageWebSearchToolCall":
type: object
properties:
id:
type: string
status:
type: string
type:
type: string
const: web_search_call
default: web_search_call
additionalProperties: false
required:
- id
- status
- type
title: >-
OpenAIResponseOutputMessageWebSearchToolCall
OpenAIResponseObjectStream:
oneOf:
- $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated'

4
llama_stack/apis/agents/agents.py
View file

@ -31,7 +31,7 @@ from llama_stack.apis.tools import ToolDef
from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
from .openai_responses import (
OpenAIResponseInputMessage,
OpenAIResponseInput,
OpenAIResponseInputTool,
OpenAIResponseObject,
OpenAIResponseObjectStream,
@ -592,7 +592,7 @@ class Agents(Protocol):
@webmethod(route="/openai/v1/responses", method="POST")
async def create_openai_response(
self,
input: str | list[OpenAIResponseInputMessage],
input: str | list[OpenAIResponseInput],
model: str,
previous_response_id: str | None = None,
store: bool | None = True,

86
llama_stack/apis/agents/openai_responses.py
View file

@ -17,6 +17,28 @@ class OpenAIResponseError(BaseModel):
message: str
@json_schema_type
class OpenAIResponseInputMessageContentText(BaseModel):
text: str
type: Literal["input_text"] = "input_text"
@json_schema_type
class OpenAIResponseInputMessageContentImage(BaseModel):
detail: Literal["low"] | Literal["high"] | Literal["auto"] = "auto"
type: Literal["input_image"] = "input_image"
# TODO: handle file_id
image_url: str | None = None
# TODO: handle file content types
OpenAIResponseInputMessageContent = Annotated[
OpenAIResponseInputMessageContentText | OpenAIResponseInputMessageContentImage,
Field(discriminator="type"),
]
register_schema(OpenAIResponseInputMessageContent, name="OpenAIResponseInputMessageContent")
@json_schema_type
class OpenAIResponseOutputMessageContentOutputText(BaseModel):
text: str
@ -31,13 +53,22 @@ register_schema(OpenAIResponseOutputMessageContent, name="OpenAIResponseOutputMe
@json_schema_type
class OpenAIResponseOutputMessage(BaseModel):
id: str
content: list[OpenAIResponseOutputMessageContent]
role: Literal["assistant"] = "assistant"
status: str
class OpenAIResponseMessage(BaseModel):
"""
Corresponds to the various Message types in the Responses API.
They are all under one type because the Responses API gives them all
the same "type" value, and there is no way to tell them apart in certain
scenarios.
"""
content: str | list[OpenAIResponseInputMessageContent] | list[OpenAIResponseOutputMessageContent]
role: Literal["system"] | Literal["developer"] | Literal["user"] | Literal["assistant"]
type: Literal["message"] = "message"
# The fields below are not used in all scenarios, but are required in others.
id: str | None = None
status: str | None = None
@json_schema_type
class OpenAIResponseOutputMessageWebSearchToolCall(BaseModel):
@ -47,7 +78,7 @@ class OpenAIResponseOutputMessageWebSearchToolCall(BaseModel):
OpenAIResponseOutput = Annotated[
OpenAIResponseOutputMessage | OpenAIResponseOutputMessageWebSearchToolCall,
OpenAIResponseMessage | OpenAIResponseOutputMessageWebSearchToolCall,
Field(discriminator="type"),
]
register_schema(OpenAIResponseOutput, name="OpenAIResponseOutput")
@ -89,33 +120,15 @@ OpenAIResponseObjectStream = Annotated[
register_schema(OpenAIResponseObjectStream, name="OpenAIResponseObjectStream")
@json_schema_type
class OpenAIResponseInputMessageContentText(BaseModel):
text: str
type: Literal["input_text"] = "input_text"
@json_schema_type
class OpenAIResponseInputMessageContentImage(BaseModel):
detail: Literal["low"] | Literal["high"] | Literal["auto"] = "auto"
type: Literal["input_image"] = "input_image"
# TODO: handle file_id
image_url: str | None = None
# TODO: handle file content types
OpenAIResponseInputMessageContent = Annotated[
OpenAIResponseInputMessageContentText | OpenAIResponseInputMessageContentImage,
Field(discriminator="type"),
OpenAIResponseInput = Annotated[
# Responses API allows output messages to be passed in as input
OpenAIResponseOutputMessageWebSearchToolCall
|
# Fallback to the generic message type as a last resort
OpenAIResponseMessage,
Field(union_mode="left_to_right"),
]
register_schema(OpenAIResponseInputMessageContent, name="OpenAIResponseInputMessageContent")
@json_schema_type
class OpenAIResponseInputMessage(BaseModel):
content: str | list[OpenAIResponseInputMessageContent]
role: Literal["system"] | Literal["developer"] | Literal["user"] | Literal["assistant"]
type: Literal["message"] | None = "message"
register_schema(OpenAIResponseInput, name="OpenAIResponseInput")
@json_schema_type
@ -133,18 +146,11 @@ OpenAIResponseInputTool = Annotated[
register_schema(OpenAIResponseInputTool, name="OpenAIResponseInputTool")
@json_schema_type
class OpenAIResponseInputItemMessage(OpenAIResponseInputMessage):
id: str
@json_schema_type
class OpenAIResponseInputItemList(BaseModel):
data: list[OpenAIResponseInputItemMessage]
data: list[OpenAIResponseInput]
object: Literal["list"] = "list"
@json_schema_type
class OpenAIResponsePreviousResponseWithInputItems(BaseModel):
input_items: OpenAIResponseInputItemList
response: OpenAIResponseObject

4
llama_stack/providers/inline/agents/meta_reference/agents.py
View file

@ -20,7 +20,7 @@ from llama_stack.apis.agents import (
AgentTurnCreateRequest,
AgentTurnResumeRequest,
Document,
OpenAIResponseInputMessage,
OpenAIResponseInput,
OpenAIResponseInputTool,
OpenAIResponseObject,
Session,
@ -311,7 +311,7 @@ class MetaReferenceAgentsImpl(Agents):
async def create_openai_response(
self,
input: str | list[OpenAIResponseInputMessage],
input: str | list[OpenAIResponseInput],
model: str,
previous_response_id: str | None = None,
store: bool | None = True,

160
llama_stack/providers/inline/agents/meta_reference/openai_responses.py
View file

@ -12,19 +12,17 @@ from typing import cast
from openai.types.chat import ChatCompletionToolParam
from llama_stack.apis.agents.openai_responses import (
OpenAIResponseInput,
OpenAIResponseInputItemList,
OpenAIResponseInputItemMessage,
OpenAIResponseInputMessage,
OpenAIResponseInputMessageContent,
OpenAIResponseInputMessageContentImage,
OpenAIResponseInputMessageContentText,
OpenAIResponseInputTool,
OpenAIResponseMessage,
OpenAIResponseObject,
OpenAIResponseObjectStream,
OpenAIResponseObjectStreamResponseCompleted,
OpenAIResponseObjectStreamResponseCreated,
OpenAIResponseOutput,
OpenAIResponseOutputMessage,
OpenAIResponseOutputMessageContentOutputText,
OpenAIResponseOutputMessageWebSearchToolCall,
OpenAIResponsePreviousResponseWithInputItems,
@ -56,62 +54,38 @@ logger = get_logger(name=__name__, category="openai_responses")
OPENAI_RESPONSES_PREFIX = "openai_responses:"
async def _convert_response_input_content_to_chat_content_parts(
input_content: list[OpenAIResponseInputMessageContent],
) -> list[OpenAIChatCompletionContentPartParam]:
"""
Convert a list of input content items to a list of chat completion content parts
"""
content_parts = []
for input_content_part in input_content:
if isinstance(input_content_part, OpenAIResponseInputMessageContentText):
content_parts.append(OpenAIChatCompletionContentPartTextParam(text=input_content_part.text))
elif isinstance(input_content_part, OpenAIResponseInputMessageContentImage):
if input_content_part.image_url:
image_url = OpenAIImageURL(url=input_content_part.image_url, detail=input_content_part.detail)
content_parts.append(OpenAIChatCompletionContentPartImageParam(image_url=image_url))
return content_parts
async def _convert_response_input_to_chat_user_content(
input: str | list[OpenAIResponseInputMessage],
) -> str | list[OpenAIChatCompletionContentPartParam]:
user_content: str | list[OpenAIChatCompletionContentPartParam] = ""
if isinstance(input, list):
user_content = []
for user_input in input:
if isinstance(user_input.content, list):
user_content.extend(await _convert_response_input_content_to_chat_content_parts(user_input.content))
else:
user_content.append(OpenAIChatCompletionContentPartTextParam(text=user_input.content))
else:
user_content = input
return user_content
async def _previous_response_to_messages(
previous_response: OpenAIResponsePreviousResponseWithInputItems,
async def _convert_response_input_to_chat_messages(
input: str | list[OpenAIResponseInput],
) -> list[OpenAIMessageParam]:
messages: list[OpenAIMessageParam] = []
for previous_message in previous_response.input_items.data:
previous_content = await _convert_response_input_content_to_chat_content_parts(previous_message.content)
if previous_message.role == "user":
converted_message = OpenAIUserMessageParam(content=previous_content)
elif previous_message.role == "assistant":
converted_message = OpenAIAssistantMessageParam(content=previous_content)
else:
# TODO: handle other message roles? unclear if system/developer roles are
# used in previous responses
continue
messages.append(converted_message)
for output_message in previous_response.response.output:
if isinstance(output_message, OpenAIResponseOutputMessage):
messages.append(OpenAIAssistantMessageParam(content=output_message.content[0].text))
content: str | list[OpenAIChatCompletionContentPartParam] = ""
if isinstance(input, list):
for input_message in input:
if isinstance(input_message.content, list):
content = []
for input_message_content in input_message.content:
if isinstance(input_message_content, OpenAIResponseInputMessageContentText):
content.append(OpenAIChatCompletionContentPartTextParam(text=input_message_content.text))
elif isinstance(input_message_content, OpenAIResponseInputMessageContentImage):
if input_message_content.image_url:
image_url = OpenAIImageURL(
url=input_message_content.image_url, detail=input_message_content.detail
)
content.append(OpenAIChatCompletionContentPartImageParam(image_url=image_url))
else:
content = input_message.content
message_type = await _get_message_type_by_role(input_message.role)
if message_type is None:
raise ValueError(
f"Llama Stack OpenAI Responses does not yet support message role '{input_message.role}' in this context"
)
messages.append(message_type(content=content))
else:
messages.append(OpenAIUserMessageParam(content=input))
return messages
async def _openai_choices_to_output_messages(choices: list[OpenAIChoice]) -> list[OpenAIResponseOutputMessage]:
async def _openai_choices_to_output_messages(choices: list[OpenAIChoice]) -> list[OpenAIResponseMessage]:
output_messages = []
for choice in choices:
output_content = ""
@ -121,10 +95,11 @@ async def _openai_choices_to_output_messages(choices: list[OpenAIChoice]) -> lis
output_content = choice.message.content.text
# TODO: handle image content
output_messages.append(
OpenAIResponseOutputMessage(
OpenAIResponseMessage(
id=f"msg_{uuid.uuid4()}",
content=[OpenAIResponseOutputMessageContentOutputText(text=output_content)],
status="completed",
role="assistant",
)
)
return output_messages
@ -160,6 +135,27 @@ class OpenAIResponsesImpl:
raise ValueError(f"OpenAI response with id '{id}' not found")
return OpenAIResponsePreviousResponseWithInputItems.model_validate_json(response_json)
async def _prepend_previous_response(
self, input: str | list[OpenAIResponseInput], previous_response_id: str | None = None
):
if previous_response_id:
previous_response_with_input = await self._get_previous_response_with_input(previous_response_id)
# previous response input items
new_input_items = previous_response_with_input.input_items.data
# previous response output items
new_input_items.extend(previous_response_with_input.response.output)
# new input items from the current request
if isinstance(input, str):
# Normalize input to a list of OpenAIResponseInputMessage objects
input = [OpenAIResponseMessage(content=input, role="user")]
new_input_items.extend(input)
input = new_input_items
return input
async def get_openai_response(
self,
id: str,
@ -169,7 +165,7 @@ class OpenAIResponsesImpl:
async def create_openai_response(
self,
input: str | list[OpenAIResponseInputMessage],
input: str | list[OpenAIResponseInput],
model: str,
previous_response_id: str | None = None,
store: bool | None = True,
@ -179,37 +175,8 @@ class OpenAIResponsesImpl:
):
stream = False if stream is None else stream
messages: list[OpenAIMessageParam] = []
if previous_response_id:
previous_response_with_input = await self._get_previous_response_with_input(previous_response_id)
messages.extend(await _previous_response_to_messages(previous_response_with_input))
# TODO: refactor this user_content parsing out into a separate method
content: str | list[OpenAIChatCompletionContentPartParam] = ""
if isinstance(input, list):
for input_message in input:
if isinstance(input_message.content, list):
content = []
for input_message_content in input_message.content:
if isinstance(input_message_content, OpenAIResponseInputMessageContentText):
content.append(OpenAIChatCompletionContentPartTextParam(text=input_message_content.text))
elif isinstance(input_message_content, OpenAIResponseInputMessageContentImage):
if input_message_content.image_url:
image_url = OpenAIImageURL(
url=input_message_content.image_url, detail=input_message_content.detail
)
content.append(OpenAIChatCompletionContentPartImageParam(image_url=image_url))
else:
content = input_message.content
message_type = await _get_message_type_by_role(input_message.role)
if message_type is None:
raise ValueError(
f"Llama Stack OpenAI Responses does not yet support message role '{input_message.role}' in this context"
)
messages.append(message_type(content=content))
else:
messages.append(OpenAIUserMessageParam(content=input))
input = await self._prepend_previous_response(input, previous_response_id)
messages = await _convert_response_input_to_chat_messages(input)
chat_tools = await self._convert_response_tools_to_chat_tools(tools) if tools else None
chat_response = await self.inference_api.openai_chat_completion(
model=model,
@ -272,22 +239,29 @@ class OpenAIResponsesImpl:
if store:
# Store in kvstore
new_input_id = f"msg_{uuid.uuid4()}"
if isinstance(input, str):
# synthesize a message from the input string
input_content = OpenAIResponseInputMessageContentText(text=input)
input_content_item = OpenAIResponseInputItemMessage(
input_content_item = OpenAIResponseMessage(
role="user",
content=[input_content],
id=f"msg_{uuid.uuid4()}",
id=new_input_id,
)
input_items_data = [input_content_item]
else:
# we already have a list of messages
input_items_data = []
for input_item in input:
input_items_data.append(
OpenAIResponseInputItemMessage(id=f"msg_{uuid.uuid4()}", **input_item.model_dump())
)
if isinstance(input_item, OpenAIResponseMessage):
# These may or may not already have an id, so dump to dict, check for id, and add if missing
input_item_dict = input_item.model_dump()
if "id" not in input_item_dict:
input_item_dict["id"] = new_input_id
input_items_data.append(OpenAIResponseMessage(**input_item_dict))
else:
input_items_data.append(input_item)
input_items = OpenAIResponseInputItemList(data=input_items_data)
prev_response = OpenAIResponsePreviousResponseWithInputItems(
input_items=input_items,

125
tests/unit/providers/agents/meta_reference/test_openai_responses.py
View file

@ -4,15 +4,19 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from unittest.mock import AsyncMock
from unittest.mock import AsyncMock, patch
import pytest
from llama_stack.apis.agents.openai_responses import (
OpenAIResponseInputMessage,
OpenAIResponseInputItemList,
OpenAIResponseInputMessageContentText,
OpenAIResponseInputToolWebSearch,
OpenAIResponseOutputMessage,
OpenAIResponseMessage,
OpenAIResponseObject,
OpenAIResponseOutputMessageContentOutputText,
OpenAIResponseOutputMessageWebSearchToolCall,
OpenAIResponsePreviousResponseWithInputItems,
)
from llama_stack.apis.inference.inference import (
OpenAIAssistantMessageParam,
@ -91,7 +95,7 @@ async def test_create_openai_response_with_string_input(openai_responses_impl, m
openai_responses_impl.persistence_store.set.assert_called_once()
assert result.model == model
assert len(result.output) == 1
assert isinstance(result.output[0], OpenAIResponseOutputMessage)
assert isinstance(result.output[0], OpenAIResponseMessage)
assert result.output[0].content[0].text == "Dublin"
@ -159,7 +163,7 @@ async def test_create_openai_response_with_string_input_with_tools(openai_respon
# Check that we got the content from our mocked tool execution result
assert len(result.output) >= 1
assert isinstance(result.output[1], OpenAIResponseOutputMessage)
assert isinstance(result.output[1], OpenAIResponseMessage)
assert result.output[1].content[0].text == "Dublin"
@ -168,9 +172,9 @@ async def test_create_openai_response_with_multiple_messages(openai_responses_im
"""Test creating an OpenAI response with multiple messages."""
# Setup
input_messages = [
OpenAIResponseInputMessage(role="developer", content="You are a helpful assistant", name=None),
OpenAIResponseInputMessage(role="user", content="Name some towns in Ireland", name=None),
OpenAIResponseInputMessage(
OpenAIResponseMessage(role="developer", content="You are a helpful assistant", name=None),
OpenAIResponseMessage(role="user", content="Name some towns in Ireland", name=None),
OpenAIResponseMessage(
role="assistant",
content=[
OpenAIResponseInputMessageContentText(text="Galway, Longford, Sligo"),
@ -178,7 +182,7 @@ async def test_create_openai_response_with_multiple_messages(openai_responses_im
],
name=None,
),
OpenAIResponseInputMessage(role="user", content="Which is the largest town in Ireland?", name=None),
OpenAIResponseMessage(role="user", content="Which is the largest town in Ireland?", name=None),
]
model = "meta-llama/Llama-3.1-8B-Instruct"
@ -207,3 +211,106 @@ async def test_create_openai_response_with_multiple_messages(openai_responses_im
assert isinstance(inference_messages[i], OpenAIAssistantMessageParam)
else:
assert isinstance(inference_messages[i], OpenAIDeveloperMessageParam)
@pytest.mark.asyncio
async def test_prepend_previous_response_none(openai_responses_impl):
"""Test prepending no previous response to a new response."""
input = await openai_responses_impl._prepend_previous_response("fake_input", None)
assert input == "fake_input"
@pytest.mark.asyncio
@patch.object(OpenAIResponsesImpl, "_get_previous_response_with_input")
async def test_prepend_previous_response_basic(get_previous_response_with_input, openai_responses_impl):
"""Test prepending a basic previous response to a new response."""
input_item_message = OpenAIResponseMessage(
id="123",
content=[OpenAIResponseInputMessageContentText(text="fake_previous_input")],
role="user",
)
input_items = OpenAIResponseInputItemList(data=[input_item_message])
response_output_message = OpenAIResponseMessage(
id="123",
content=[OpenAIResponseOutputMessageContentOutputText(text="fake_response")],
status="completed",
role="assistant",
)
response = OpenAIResponseObject(
created_at=1,
id="resp_123",
model="fake_model",
output=[response_output_message],
status="completed",
)
previous_response = OpenAIResponsePreviousResponseWithInputItems(
input_items=input_items,
response=response,
)
get_previous_response_with_input.return_value = previous_response
input = await openai_responses_impl._prepend_previous_response("fake_input", "resp_123")
assert len(input) == 3
# Check for previous input
assert isinstance(input[0], OpenAIResponseMessage)
assert input[0].content[0].text == "fake_previous_input"
# Check for previous output
assert isinstance(input[1], OpenAIResponseMessage)
assert input[1].content[0].text == "fake_response"
# Check for new input
assert isinstance(input[2], OpenAIResponseMessage)
assert input[2].content == "fake_input"
@pytest.mark.asyncio
@patch.object(OpenAIResponsesImpl, "_get_previous_response_with_input")
async def test_prepend_previous_response_web_search(get_previous_response_with_input, openai_responses_impl):
"""Test prepending a web search previous response to a new response."""
input_item_message = OpenAIResponseMessage(
id="123",
content=[OpenAIResponseInputMessageContentText(text="fake_previous_input")],
role="user",
)
input_items = OpenAIResponseInputItemList(data=[input_item_message])
output_web_search = OpenAIResponseOutputMessageWebSearchToolCall(
id="ws_123",
status="completed",
)
output_message = OpenAIResponseMessage(
id="123",
content=[OpenAIResponseOutputMessageContentOutputText(text="fake_web_search_response")],
status="completed",
role="assistant",
)
response = OpenAIResponseObject(
created_at=1,
id="resp_123",
model="fake_model",
output=[output_web_search, output_message],
status="completed",
)
previous_response = OpenAIResponsePreviousResponseWithInputItems(
input_items=input_items,
response=response,
)
get_previous_response_with_input.return_value = previous_response
input_messages = [OpenAIResponseMessage(content="fake_input", role="user")]
input = await openai_responses_impl._prepend_previous_response(input_messages, "resp_123")
assert len(input) == 4
# Check for previous input
assert isinstance(input[0], OpenAIResponseMessage)
assert input[0].content[0].text == "fake_previous_input"
# Check for previous output web search tool call
assert isinstance(input[1], OpenAIResponseOutputMessageWebSearchToolCall)
# Check for previous output web search response
assert isinstance(input[2], OpenAIResponseMessage)
assert input[2].content[0].text == "fake_web_search_response"
# Check for new input
assert isinstance(input[3], OpenAIResponseMessage)
assert input[3].content == "fake_input"