feat(responses): add MCP argument streaming and content part events (#3136)

# What does this PR do?

Adds content part streaming events to the OpenAI-compatible Responses API to support more granular streaming of response content. This introduces:

1. New schema types for content parts: `OpenAIResponseContentPart` with variants for text output and refusals

2. New streaming event types:
   - `OpenAIResponseObjectStreamResponseContentPartAdded` for when content parts begin
   - `OpenAIResponseObjectStreamResponseContentPartDone` for when content parts complete

3. Implementation in the reference provider to emit these events during streaming responses. Also emits MCP arguments just like function call ones.


## Test Plan

Updated existing streaming tests to verify content part events are properly emitted
This commit is contained in:
Ashwin Bharambe 2025-08-13 16:34:26 -07:00 committed by GitHub
parent 8638537d14
commit e1e161553c
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
6 changed files with 480 additions and 35 deletions

View file

@ -8821,6 +8821,61 @@
"title": "OpenAIResponseOutputMessageMCPListTools",
"description": "MCP list tools output message containing available tools from an MCP server."
},
"OpenAIResponseContentPart": {
"oneOf": [
{
"$ref": "#/components/schemas/OpenAIResponseContentPartOutputText"
},
{
"$ref": "#/components/schemas/OpenAIResponseContentPartRefusal"
}
],
"discriminator": {
"propertyName": "type",
"mapping": {
"output_text": "#/components/schemas/OpenAIResponseContentPartOutputText",
"refusal": "#/components/schemas/OpenAIResponseContentPartRefusal"
}
}
},
"OpenAIResponseContentPartOutputText": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "output_text",
"default": "output_text"
},
"text": {
"type": "string"
}
},
"additionalProperties": false,
"required": [
"type",
"text"
],
"title": "OpenAIResponseContentPartOutputText"
},
"OpenAIResponseContentPartRefusal": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "refusal",
"default": "refusal"
},
"refusal": {
"type": "string"
}
},
"additionalProperties": false,
"required": [
"type",
"refusal"
],
"title": "OpenAIResponseContentPartRefusal"
},
"OpenAIResponseObjectStream": {
"oneOf": [
{
@ -8877,6 +8932,12 @@
{
"$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallCompleted"
},
{
"$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseContentPartAdded"
},
{
"$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseContentPartDone"
},
{
"$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseCompleted"
}
@ -8902,6 +8963,8 @@
"response.mcp_call.in_progress": "#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallInProgress",
"response.mcp_call.failed": "#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallFailed",
"response.mcp_call.completed": "#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallCompleted",
"response.content_part.added": "#/components/schemas/OpenAIResponseObjectStreamResponseContentPartAdded",
"response.content_part.done": "#/components/schemas/OpenAIResponseObjectStreamResponseContentPartDone",
"response.completed": "#/components/schemas/OpenAIResponseObjectStreamResponseCompleted"
}
}
@ -8928,6 +8991,80 @@
"title": "OpenAIResponseObjectStreamResponseCompleted",
"description": "Streaming event indicating a response has been completed."
},
"OpenAIResponseObjectStreamResponseContentPartAdded": {
"type": "object",
"properties": {
"response_id": {
"type": "string",
"description": "Unique identifier of the response containing this content"
},
"item_id": {
"type": "string",
"description": "Unique identifier of the output item containing this content part"
},
"part": {
"$ref": "#/components/schemas/OpenAIResponseContentPart",
"description": "The content part that was added"
},
"sequence_number": {
"type": "integer",
"description": "Sequential number for ordering streaming events"
},
"type": {
"type": "string",
"const": "response.content_part.added",
"default": "response.content_part.added",
"description": "Event type identifier, always \"response.content_part.added\""
}
},
"additionalProperties": false,
"required": [
"response_id",
"item_id",
"part",
"sequence_number",
"type"
],
"title": "OpenAIResponseObjectStreamResponseContentPartAdded",
"description": "Streaming event for when a new content part is added to a response item."
},
"OpenAIResponseObjectStreamResponseContentPartDone": {
"type": "object",
"properties": {
"response_id": {
"type": "string",
"description": "Unique identifier of the response containing this content"
},
"item_id": {
"type": "string",
"description": "Unique identifier of the output item containing this content part"
},
"part": {
"$ref": "#/components/schemas/OpenAIResponseContentPart",
"description": "The completed content part"
},
"sequence_number": {
"type": "integer",
"description": "Sequential number for ordering streaming events"
},
"type": {
"type": "string",
"const": "response.content_part.done",
"default": "response.content_part.done",
"description": "Event type identifier, always \"response.content_part.done\""
}
},
"additionalProperties": false,
"required": [
"response_id",
"item_id",
"part",
"sequence_number",
"type"
],
"title": "OpenAIResponseObjectStreamResponseContentPartDone",
"description": "Streaming event for when a content part is completed."
},
"OpenAIResponseObjectStreamResponseCreated": {
"type": "object",
"properties": {

View file

@ -6441,6 +6441,43 @@ components:
title: OpenAIResponseOutputMessageMCPListTools
description: >-
MCP list tools output message containing available tools from an MCP server.
OpenAIResponseContentPart:
oneOf:
- $ref: '#/components/schemas/OpenAIResponseContentPartOutputText'
- $ref: '#/components/schemas/OpenAIResponseContentPartRefusal'
discriminator:
propertyName: type
mapping:
output_text: '#/components/schemas/OpenAIResponseContentPartOutputText'
refusal: '#/components/schemas/OpenAIResponseContentPartRefusal'
OpenAIResponseContentPartOutputText:
type: object
properties:
type:
type: string
const: output_text
default: output_text
text:
type: string
additionalProperties: false
required:
- type
- text
title: OpenAIResponseContentPartOutputText
OpenAIResponseContentPartRefusal:
type: object
properties:
type:
type: string
const: refusal
default: refusal
refusal:
type: string
additionalProperties: false
required:
- type
- refusal
title: OpenAIResponseContentPartRefusal
OpenAIResponseObjectStream:
oneOf:
- $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated'
@ -6461,6 +6498,8 @@ components:
- $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallInProgress'
- $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallFailed'
- $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallCompleted'
- $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseContentPartAdded'
- $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseContentPartDone'
- $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted'
discriminator:
propertyName: type
@ -6483,6 +6522,8 @@ components:
response.mcp_call.in_progress: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallInProgress'
response.mcp_call.failed: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallFailed'
response.mcp_call.completed: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallCompleted'
response.content_part.added: '#/components/schemas/OpenAIResponseObjectStreamResponseContentPartAdded'
response.content_part.done: '#/components/schemas/OpenAIResponseObjectStreamResponseContentPartDone'
response.completed: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted'
"OpenAIResponseObjectStreamResponseCompleted":
type: object
@ -6504,6 +6545,76 @@ components:
OpenAIResponseObjectStreamResponseCompleted
description: >-
Streaming event indicating a response has been completed.
"OpenAIResponseObjectStreamResponseContentPartAdded":
type: object
properties:
response_id:
type: string
description: >-
Unique identifier of the response containing this content
item_id:
type: string
description: >-
Unique identifier of the output item containing this content part
part:
$ref: '#/components/schemas/OpenAIResponseContentPart'
description: The content part that was added
sequence_number:
type: integer
description: >-
Sequential number for ordering streaming events
type:
type: string
const: response.content_part.added
default: response.content_part.added
description: >-
Event type identifier, always "response.content_part.added"
additionalProperties: false
required:
- response_id
- item_id
- part
- sequence_number
- type
title: >-
OpenAIResponseObjectStreamResponseContentPartAdded
description: >-
Streaming event for when a new content part is added to a response item.
"OpenAIResponseObjectStreamResponseContentPartDone":
type: object
properties:
response_id:
type: string
description: >-
Unique identifier of the response containing this content
item_id:
type: string
description: >-
Unique identifier of the output item containing this content part
part:
$ref: '#/components/schemas/OpenAIResponseContentPart'
description: The completed content part
sequence_number:
type: integer
description: >-
Sequential number for ordering streaming events
type:
type: string
const: response.content_part.done
default: response.content_part.done
description: >-
Event type identifier, always "response.content_part.done"
additionalProperties: false
required:
- response_id
- item_id
- part
- sequence_number
- type
title: >-
OpenAIResponseObjectStreamResponseContentPartDone
description: >-
Streaming event for when a content part is completed.
"OpenAIResponseObjectStreamResponseCreated":
type: object
properties:

View file

@ -623,6 +623,62 @@ class OpenAIResponseObjectStreamResponseMcpCallCompleted(BaseModel):
type: Literal["response.mcp_call.completed"] = "response.mcp_call.completed"
@json_schema_type
class OpenAIResponseContentPartOutputText(BaseModel):
type: Literal["output_text"] = "output_text"
text: str
# TODO: add annotations, logprobs, etc.
@json_schema_type
class OpenAIResponseContentPartRefusal(BaseModel):
type: Literal["refusal"] = "refusal"
refusal: str
OpenAIResponseContentPart = Annotated[
OpenAIResponseContentPartOutputText | OpenAIResponseContentPartRefusal,
Field(discriminator="type"),
]
register_schema(OpenAIResponseContentPart, name="OpenAIResponseContentPart")
@json_schema_type
class OpenAIResponseObjectStreamResponseContentPartAdded(BaseModel):
"""Streaming event for when a new content part is added to a response item.
:param response_id: Unique identifier of the response containing this content
:param item_id: Unique identifier of the output item containing this content part
:param part: The content part that was added
:param sequence_number: Sequential number for ordering streaming events
:param type: Event type identifier, always "response.content_part.added"
"""
response_id: str
item_id: str
part: OpenAIResponseContentPart
sequence_number: int
type: Literal["response.content_part.added"] = "response.content_part.added"
@json_schema_type
class OpenAIResponseObjectStreamResponseContentPartDone(BaseModel):
"""Streaming event for when a content part is completed.
:param response_id: Unique identifier of the response containing this content
:param item_id: Unique identifier of the output item containing this content part
:param part: The completed content part
:param sequence_number: Sequential number for ordering streaming events
:param type: Event type identifier, always "response.content_part.done"
"""
response_id: str
item_id: str
part: OpenAIResponseContentPart
sequence_number: int
type: Literal["response.content_part.done"] = "response.content_part.done"
OpenAIResponseObjectStream = Annotated[
OpenAIResponseObjectStreamResponseCreated
| OpenAIResponseObjectStreamResponseOutputItemAdded
@ -642,6 +698,8 @@ OpenAIResponseObjectStream = Annotated[
| OpenAIResponseObjectStreamResponseMcpCallInProgress
| OpenAIResponseObjectStreamResponseMcpCallFailed
| OpenAIResponseObjectStreamResponseMcpCallCompleted
| OpenAIResponseObjectStreamResponseContentPartAdded
| OpenAIResponseObjectStreamResponseContentPartDone
| OpenAIResponseObjectStreamResponseCompleted,
Field(discriminator="type"),
]

View file

@ -20,6 +20,7 @@ from llama_stack.apis.agents.openai_responses import (
ListOpenAIResponseInputItem,
ListOpenAIResponseObject,
OpenAIDeleteResponseObject,
OpenAIResponseContentPartOutputText,
OpenAIResponseInput,
OpenAIResponseInputFunctionToolCallOutput,
OpenAIResponseInputMessageContent,
@ -32,9 +33,13 @@ from llama_stack.apis.agents.openai_responses import (
OpenAIResponseObject,
OpenAIResponseObjectStream,
OpenAIResponseObjectStreamResponseCompleted,
OpenAIResponseObjectStreamResponseContentPartAdded,
OpenAIResponseObjectStreamResponseContentPartDone,
OpenAIResponseObjectStreamResponseCreated,
OpenAIResponseObjectStreamResponseFunctionCallArgumentsDelta,
OpenAIResponseObjectStreamResponseFunctionCallArgumentsDone,
OpenAIResponseObjectStreamResponseMcpCallArgumentsDelta,
OpenAIResponseObjectStreamResponseMcpCallArgumentsDone,
OpenAIResponseObjectStreamResponseMcpCallCompleted,
OpenAIResponseObjectStreamResponseMcpCallFailed,
OpenAIResponseObjectStreamResponseMcpCallInProgress,
@ -475,6 +480,8 @@ class OpenAIResponsesImpl:
message_item_id = f"msg_{uuid.uuid4()}"
# Track tool call items for streaming events
tool_call_item_ids: dict[int, str] = {}
# Track content parts for streaming events
content_part_emitted = False
async for chunk in completion_result:
chat_response_id = chunk.id
@ -483,6 +490,18 @@ class OpenAIResponsesImpl:
for chunk_choice in chunk.choices:
# Emit incremental text content as delta events
if chunk_choice.delta.content:
# Emit content_part.added event for first text chunk
if not content_part_emitted:
content_part_emitted = True
sequence_number += 1
yield OpenAIResponseObjectStreamResponseContentPartAdded(
response_id=response_id,
item_id=message_item_id,
part=OpenAIResponseContentPartOutputText(
text="", # Will be filled incrementally via text deltas
),
sequence_number=sequence_number,
)
sequence_number += 1
yield OpenAIResponseObjectStreamResponseOutputTextDelta(
content_index=0,
@ -529,16 +548,33 @@ class OpenAIResponsesImpl:
sequence_number=sequence_number,
)
# Stream function call arguments as they arrive
# Stream tool call arguments as they arrive (differentiate between MCP and function calls)
if tool_call.function and tool_call.function.arguments:
tool_call_item_id = tool_call_item_ids[tool_call.index]
sequence_number += 1
yield OpenAIResponseObjectStreamResponseFunctionCallArgumentsDelta(
delta=tool_call.function.arguments,
item_id=tool_call_item_id,
output_index=len(output_messages),
sequence_number=sequence_number,
# Check if this is an MCP tool call
is_mcp_tool = (
ctx.mcp_tool_to_server
and tool_call.function.name
and tool_call.function.name in ctx.mcp_tool_to_server
)
if is_mcp_tool:
# Emit MCP-specific argument delta event
yield OpenAIResponseObjectStreamResponseMcpCallArgumentsDelta(
delta=tool_call.function.arguments,
item_id=tool_call_item_id,
output_index=len(output_messages),
sequence_number=sequence_number,
)
else:
# Emit function call argument delta event
yield OpenAIResponseObjectStreamResponseFunctionCallArgumentsDelta(
delta=tool_call.function.arguments,
item_id=tool_call_item_id,
output_index=len(output_messages),
sequence_number=sequence_number,
)
# Accumulate arguments for final response (only for subsequent chunks)
if not is_new_tool_call:
@ -546,27 +582,55 @@ class OpenAIResponsesImpl:
response_tool_call.function.arguments or ""
) + tool_call.function.arguments
# Emit function_call_arguments.done events for completed tool calls
# Emit arguments.done events for completed tool calls (differentiate between MCP and function calls)
for tool_call_index in sorted(chat_response_tool_calls.keys()):
tool_call_item_id = tool_call_item_ids[tool_call_index]
final_arguments = chat_response_tool_calls[tool_call_index].function.arguments or ""
tool_call_name = chat_response_tool_calls[tool_call_index].function.name
# Check if this is an MCP tool call
is_mcp_tool = ctx.mcp_tool_to_server and tool_call_name and tool_call_name in ctx.mcp_tool_to_server
sequence_number += 1
yield OpenAIResponseObjectStreamResponseFunctionCallArgumentsDone(
arguments=final_arguments,
item_id=tool_call_item_id,
output_index=len(output_messages),
sequence_number=sequence_number,
)
if is_mcp_tool:
# Emit MCP-specific argument done event
yield OpenAIResponseObjectStreamResponseMcpCallArgumentsDone(
arguments=final_arguments,
item_id=tool_call_item_id,
output_index=len(output_messages),
sequence_number=sequence_number,
)
else:
# Emit function call argument done event
yield OpenAIResponseObjectStreamResponseFunctionCallArgumentsDone(
arguments=final_arguments,
item_id=tool_call_item_id,
output_index=len(output_messages),
sequence_number=sequence_number,
)
# Convert collected chunks to complete response
if chat_response_tool_calls:
tool_calls = [chat_response_tool_calls[i] for i in sorted(chat_response_tool_calls.keys())]
# when there are tool calls, we need to clear the content
chat_response_content = []
else:
tool_calls = None
# Emit content_part.done event if text content was streamed (before content gets cleared)
if content_part_emitted:
final_text = "".join(chat_response_content)
sequence_number += 1
yield OpenAIResponseObjectStreamResponseContentPartDone(
response_id=response_id,
item_id=message_item_id,
part=OpenAIResponseContentPartOutputText(
text=final_text,
),
sequence_number=sequence_number,
)
# Clear content when there are tool calls (OpenAI spec behavior)
if chat_response_tool_calls:
chat_response_content = []
assistant_message = OpenAIAssistantMessageParam(
content="".join(chat_response_content),
tool_calls=tool_calls,

View file

@ -590,9 +590,17 @@ def test_response_streaming_multi_turn_tool_execution(compat_client, text_model_
# Verify tool call streaming events are present
chunk_types = [chunk.type for chunk in chunks]
# Should have function call arguments delta events for tool calls
delta_events = [chunk for chunk in chunks if chunk.type == "response.function_call_arguments.delta"]
done_events = [chunk for chunk in chunks if chunk.type == "response.function_call_arguments.done"]
# Should have function call or MCP arguments delta/done events for tool calls
delta_events = [
chunk
for chunk in chunks
if chunk.type in ["response.function_call_arguments.delta", "response.mcp_call.arguments.delta"]
]
done_events = [
chunk
for chunk in chunks
if chunk.type in ["response.function_call_arguments.done", "response.mcp_call.arguments.done"]
]
# Should have output item events for tool calls
item_added_events = [chunk for chunk in chunks if chunk.type == "response.output_item.added"]
@ -606,8 +614,12 @@ def test_response_streaming_multi_turn_tool_execution(compat_client, text_model_
assert len(chunks) > 10, f"Expected rich streaming with many events, got only {len(chunks)} chunks"
# Since this test involves MCP tool calls, we should see streaming events
assert len(delta_events) > 0, f"Expected function_call_arguments.delta events, got chunk types: {chunk_types}"
assert len(done_events) > 0, f"Expected function_call_arguments.done events, got chunk types: {chunk_types}"
assert len(delta_events) > 0, (
f"Expected function_call_arguments.delta or mcp_call.arguments.delta events, got chunk types: {chunk_types}"
)
assert len(done_events) > 0, (
f"Expected function_call_arguments.done or mcp_call.arguments.done events, got chunk types: {chunk_types}"
)
# Should have output item events for function calls
assert len(item_added_events) > 0, f"Expected response.output_item.added events, got chunk types: {chunk_types}"
@ -670,22 +682,32 @@ def test_response_streaming_multi_turn_tool_execution(compat_client, text_model_
assert isinstance(done_event.output_index, int), "Output index should be integer"
assert done_event.output_index >= 0, "Output index should be non-negative"
# Group function call argument events by item_id (these should have proper tracking)
function_call_events_by_item_id = {}
# Group function call and MCP argument events by item_id (these should have proper tracking)
argument_events_by_item_id = {}
for chunk in chunks:
if hasattr(chunk, "item_id") and chunk.type in [
"response.function_call_arguments.delta",
"response.function_call_arguments.done",
"response.mcp_call.arguments.delta",
"response.mcp_call.arguments.done",
]:
item_id = chunk.item_id
if item_id not in function_call_events_by_item_id:
function_call_events_by_item_id[item_id] = []
function_call_events_by_item_id[item_id].append(chunk)
if item_id not in argument_events_by_item_id:
argument_events_by_item_id[item_id] = []
argument_events_by_item_id[item_id].append(chunk)
for item_id, related_events in function_call_events_by_item_id.items():
# Should have at least one delta and one done event for a complete function call
delta_events = [e for e in related_events if e.type == "response.function_call_arguments.delta"]
done_events = [e for e in related_events if e.type == "response.function_call_arguments.done"]
for item_id, related_events in argument_events_by_item_id.items():
# Should have at least one delta and one done event for a complete tool call
delta_events = [
e
for e in related_events
if e.type in ["response.function_call_arguments.delta", "response.mcp_call.arguments.delta"]
]
done_events = [
e
for e in related_events
if e.type in ["response.function_call_arguments.done", "response.mcp_call.arguments.done"]
]
assert len(delta_events) > 0, f"Item {item_id} should have at least one delta event"
assert len(done_events) == 1, f"Item {item_id} should have exactly one done event"
@ -694,6 +716,33 @@ def test_response_streaming_multi_turn_tool_execution(compat_client, text_model_
for event in related_events:
assert event.item_id == item_id, f"Event should have consistent item_id {item_id}, got {event.item_id}"
# Verify content part events if they exist (for text streaming)
content_part_added_events = [chunk for chunk in chunks if chunk.type == "response.content_part.added"]
content_part_done_events = [chunk for chunk in chunks if chunk.type == "response.content_part.done"]
# Content part events should be paired (if any exist)
if len(content_part_added_events) > 0:
assert len(content_part_done_events) > 0, (
"Should have content_part.done events if content_part.added events exist"
)
# Verify content part event structure
for added_event in content_part_added_events:
assert hasattr(added_event, "response_id"), "Content part added event should have response_id"
assert hasattr(added_event, "item_id"), "Content part added event should have item_id"
assert hasattr(added_event, "part"), "Content part added event should have part"
# TODO: enable this after the client types are updated
# assert added_event.part.type == "output_text", "Content part should be an output_text"
for done_event in content_part_done_events:
assert hasattr(done_event, "response_id"), "Content part done event should have response_id"
assert hasattr(done_event, "item_id"), "Content part done event should have item_id"
assert hasattr(done_event, "part"), "Content part done event should have part"
# TODO: enable this after the client types are updated
# assert len(done_event.part.text) > 0, "Content part should have text when done"
# Basic pairing check: each output_item.added should be followed by some activity
# (but we can't enforce strict 1:1 pairing due to the complexity of multi-turn scenarios)
assert len(item_added_events) > 0, "Should have at least one output_item.added event"

View file

@ -136,9 +136,12 @@ async def test_create_openai_response_with_string_input(openai_responses_impl, m
input=input_text,
model=model,
temperature=0.1,
stream=True, # Enable streaming to test content part events
)
# Verify
# For streaming response, collect all chunks
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)],
@ -147,11 +150,32 @@ async def test_create_openai_response_with_string_input(openai_responses_impl, m
stream=True,
temperature=0.1,
)
# Should have content part events for text streaming
# Expected: response.created, content_part.added, output_text.delta, content_part.done, response.completed
assert len(chunks) >= 4
assert chunks[0].type == "response.created"
# Check for content part events
content_part_added_events = [c for c in chunks if c.type == "response.content_part.added"]
content_part_done_events = [c for c in chunks if c.type == "response.content_part.done"]
text_delta_events = [c for c in chunks if c.type == "response.output_text.delta"]
assert len(content_part_added_events) >= 1, "Should have content_part.added event for text"
assert len(content_part_done_events) >= 1, "Should have content_part.done event for text"
assert len(text_delta_events) >= 1, "Should have text delta events"
# Verify final event is completion
assert chunks[-1].type == "response.completed"
# When streaming, the final response is in the last chunk
final_response = chunks[-1].response
assert final_response.model == model
assert len(final_response.output) == 1
assert isinstance(final_response.output[0], OpenAIResponseMessage)
openai_responses_impl.responses_store.store_response_object.assert_called_once()
assert result.model == model
assert len(result.output) == 1
assert isinstance(result.output[0], OpenAIResponseMessage)
assert result.output[0].content[0].text == "Dublin"
assert final_response.output[0].content[0].text == "Dublin"
async def test_create_openai_response_with_string_input_with_tools(openai_responses_impl, mock_inference_api):
@ -272,6 +296,8 @@ async def test_create_openai_response_with_tool_call_type_none(openai_responses_
# Check that we got the content from our mocked tool execution result
chunks = [chunk async for chunk in result]
# Verify event types
# Should have: response.created, output_item.added, function_call_arguments.delta,
# function_call_arguments.done, output_item.done, response.completed
assert len(chunks) == 6