wehub-resource-sync
698 lines
26 KiB
Python
698 lines
26 KiB
Python
from collections.abc import AsyncIterator
|
|
|
|
import pytest
|
|
from openai.types.chat.chat_completion_chunk import (
|
|
ChatCompletionChunk,
|
|
Choice,
|
|
ChoiceDelta,
|
|
ChoiceDeltaToolCall,
|
|
ChoiceDeltaToolCallFunction,
|
|
)
|
|
from openai.types.completion_usage import (
|
|
CompletionTokensDetails,
|
|
CompletionUsage,
|
|
PromptTokensDetails,
|
|
)
|
|
from openai.types.responses import (
|
|
Response,
|
|
ResponseCompletedEvent,
|
|
ResponseContentPartAddedEvent,
|
|
ResponseFunctionToolCall,
|
|
ResponseOutputMessage,
|
|
ResponseOutputRefusal,
|
|
ResponseOutputText,
|
|
ResponseReasoningItem,
|
|
ResponseRefusalDeltaEvent,
|
|
)
|
|
|
|
from agents.extensions.models.litellm_model import LitellmModel
|
|
from agents.extensions.models.litellm_provider import LitellmProvider
|
|
from agents.model_settings import ModelSettings
|
|
from agents.models.interface import ModelTracing
|
|
|
|
|
|
@pytest.mark.allow_call_model_methods
|
|
@pytest.mark.asyncio
|
|
async def test_stream_response_yields_events_for_text_content(monkeypatch) -> None:
|
|
"""
|
|
Validate that `stream_response` emits the correct sequence of events when
|
|
streaming a simple assistant message consisting of plain text content.
|
|
We simulate two chunks of text returned from the chat completion stream.
|
|
"""
|
|
# Create two chunks that will be emitted by the fake stream.
|
|
chunk1 = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(content="He"))],
|
|
)
|
|
# Mark last chunk with usage so stream_response knows this is final.
|
|
chunk2 = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(content="llo"))],
|
|
usage=CompletionUsage(
|
|
completion_tokens=5,
|
|
prompt_tokens=7,
|
|
total_tokens=12,
|
|
completion_tokens_details=CompletionTokensDetails(reasoning_tokens=2),
|
|
prompt_tokens_details=PromptTokensDetails(cached_tokens=6),
|
|
),
|
|
)
|
|
|
|
async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
|
|
for c in (chunk1, chunk2):
|
|
yield c
|
|
|
|
# Patch _fetch_response to inject our fake stream
|
|
async def patched_fetch_response(self, *args, **kwargs):
|
|
# `_fetch_response` is expected to return a Response skeleton and the async stream
|
|
resp = Response(
|
|
id="resp-id",
|
|
created_at=0,
|
|
model="fake-model",
|
|
object="response",
|
|
output=[],
|
|
tool_choice="none",
|
|
tools=[],
|
|
parallel_tool_calls=False,
|
|
)
|
|
return resp, fake_stream()
|
|
|
|
monkeypatch.setattr(LitellmModel, "_fetch_response", patched_fetch_response)
|
|
model = LitellmProvider().get_model("gpt-4")
|
|
output_events = []
|
|
async for event in model.stream_response(
|
|
system_instructions=None,
|
|
input="",
|
|
model_settings=ModelSettings(),
|
|
tools=[],
|
|
output_schema=None,
|
|
handoffs=[],
|
|
tracing=ModelTracing.DISABLED,
|
|
previous_response_id=None,
|
|
conversation_id=None,
|
|
prompt=None,
|
|
):
|
|
output_events.append(event)
|
|
# We expect a response.created, then a response.output_item.added, content part added,
|
|
# two content delta events (for "He" and "llo"), a content part done, the assistant message
|
|
# output_item.done, and finally response.completed.
|
|
# There should be 8 events in total.
|
|
assert len(output_events) == 8
|
|
# First event indicates creation.
|
|
assert output_events[0].type == "response.created"
|
|
# The output item added and content part added events should mark the assistant message.
|
|
assert output_events[1].type == "response.output_item.added"
|
|
assert output_events[2].type == "response.content_part.added"
|
|
# Two text delta events.
|
|
assert output_events[3].type == "response.output_text.delta"
|
|
assert output_events[3].delta == "He"
|
|
assert output_events[4].type == "response.output_text.delta"
|
|
assert output_events[4].delta == "llo"
|
|
# After streaming, the content part and item should be marked done.
|
|
assert output_events[5].type == "response.content_part.done"
|
|
assert output_events[6].type == "response.output_item.done"
|
|
# Last event indicates completion of the stream.
|
|
assert output_events[7].type == "response.completed"
|
|
# The completed response should have one output message with full text.
|
|
completed_resp = output_events[7].response
|
|
assert isinstance(completed_resp.output[0], ResponseOutputMessage)
|
|
assert isinstance(completed_resp.output[0].content[0], ResponseOutputText)
|
|
assert completed_resp.output[0].content[0].text == "Hello"
|
|
|
|
assert completed_resp.usage, "usage should not be None"
|
|
assert completed_resp.usage.input_tokens == 7
|
|
assert completed_resp.usage.output_tokens == 5
|
|
assert completed_resp.usage.total_tokens == 12
|
|
assert completed_resp.usage.input_tokens_details.cached_tokens == 6
|
|
assert completed_resp.usage.output_tokens_details.reasoning_tokens == 2
|
|
|
|
|
|
@pytest.mark.allow_call_model_methods
|
|
@pytest.mark.asyncio
|
|
async def test_stream_response_yields_events_for_refusal_content(monkeypatch) -> None:
|
|
"""
|
|
Validate that when the model streams a refusal string instead of normal content,
|
|
`stream_response` emits the appropriate sequence of events including
|
|
`response.refusal.delta` events for each chunk of the refusal message and
|
|
constructs a completed assistant message with a `ResponseOutputRefusal` part.
|
|
"""
|
|
# Simulate refusal text coming in two pieces, like content but using the `refusal`
|
|
# field on the delta rather than `content`.
|
|
chunk1 = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(refusal="No"))],
|
|
)
|
|
chunk2 = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(refusal="Thanks"))],
|
|
usage=CompletionUsage(completion_tokens=2, prompt_tokens=2, total_tokens=4),
|
|
)
|
|
|
|
async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
|
|
for c in (chunk1, chunk2):
|
|
yield c
|
|
|
|
async def patched_fetch_response(self, *args, **kwargs):
|
|
resp = Response(
|
|
id="resp-id",
|
|
created_at=0,
|
|
model="fake-model",
|
|
object="response",
|
|
output=[],
|
|
tool_choice="none",
|
|
tools=[],
|
|
parallel_tool_calls=False,
|
|
)
|
|
return resp, fake_stream()
|
|
|
|
monkeypatch.setattr(LitellmModel, "_fetch_response", patched_fetch_response)
|
|
model = LitellmProvider().get_model("gpt-4")
|
|
output_events = []
|
|
async for event in model.stream_response(
|
|
system_instructions=None,
|
|
input="",
|
|
model_settings=ModelSettings(),
|
|
tools=[],
|
|
output_schema=None,
|
|
handoffs=[],
|
|
tracing=ModelTracing.DISABLED,
|
|
previous_response_id=None,
|
|
conversation_id=None,
|
|
prompt=None,
|
|
):
|
|
output_events.append(event)
|
|
# Expect sequence similar to text: created, output_item.added, content part added,
|
|
# two refusal delta events, content part done, output_item.done, completed.
|
|
assert len(output_events) == 8
|
|
assert output_events[0].type == "response.created"
|
|
assert output_events[1].type == "response.output_item.added"
|
|
assert output_events[2].type == "response.content_part.added"
|
|
assert output_events[3].type == "response.refusal.delta"
|
|
assert output_events[3].delta == "No"
|
|
assert output_events[4].type == "response.refusal.delta"
|
|
assert output_events[4].delta == "Thanks"
|
|
assert output_events[5].type == "response.content_part.done"
|
|
assert output_events[6].type == "response.output_item.done"
|
|
assert output_events[7].type == "response.completed"
|
|
completed_resp = output_events[7].response
|
|
assert isinstance(completed_resp.output[0], ResponseOutputMessage)
|
|
refusal_part = completed_resp.output[0].content[0]
|
|
assert isinstance(refusal_part, ResponseOutputRefusal)
|
|
assert refusal_part.refusal == "NoThanks"
|
|
|
|
|
|
@pytest.mark.allow_call_model_methods
|
|
@pytest.mark.asyncio
|
|
async def test_stream_response_yields_events_for_tool_call(monkeypatch) -> None:
|
|
"""
|
|
Validate that `stream_response` emits the correct sequence of events when
|
|
the model is streaming a function/tool call instead of plain text.
|
|
The function call will be split across two chunks.
|
|
"""
|
|
# Simulate a single tool call with complete function name in first chunk
|
|
# and arguments split across chunks (reflecting real API behavior)
|
|
tool_call_delta1 = ChoiceDeltaToolCall(
|
|
index=0,
|
|
id="tool-id",
|
|
function=ChoiceDeltaToolCallFunction(name="my_func", arguments="arg1"),
|
|
type="function",
|
|
)
|
|
tool_call_delta2 = ChoiceDeltaToolCall(
|
|
index=0,
|
|
id="tool-id",
|
|
function=ChoiceDeltaToolCallFunction(name=None, arguments="arg2"),
|
|
type="function",
|
|
)
|
|
chunk1 = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta1]))],
|
|
)
|
|
chunk2 = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta2]))],
|
|
usage=CompletionUsage(completion_tokens=1, prompt_tokens=1, total_tokens=2),
|
|
)
|
|
|
|
async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
|
|
for c in (chunk1, chunk2):
|
|
yield c
|
|
|
|
async def patched_fetch_response(self, *args, **kwargs):
|
|
resp = Response(
|
|
id="resp-id",
|
|
created_at=0,
|
|
model="fake-model",
|
|
object="response",
|
|
output=[],
|
|
tool_choice="none",
|
|
tools=[],
|
|
parallel_tool_calls=False,
|
|
)
|
|
return resp, fake_stream()
|
|
|
|
monkeypatch.setattr(LitellmModel, "_fetch_response", patched_fetch_response)
|
|
model = LitellmProvider().get_model("gpt-4")
|
|
output_events = []
|
|
async for event in model.stream_response(
|
|
system_instructions=None,
|
|
input="",
|
|
model_settings=ModelSettings(),
|
|
tools=[],
|
|
output_schema=None,
|
|
handoffs=[],
|
|
tracing=ModelTracing.DISABLED,
|
|
previous_response_id=None,
|
|
conversation_id=None,
|
|
prompt=None,
|
|
):
|
|
output_events.append(event)
|
|
# Sequence should be: response.created, then after loop we expect function call-related events:
|
|
# one response.output_item.added for function call, a response.function_call_arguments.delta,
|
|
# a response.output_item.done, and finally response.completed.
|
|
assert output_events[0].type == "response.created"
|
|
# The next three events are about the tool call.
|
|
assert output_events[1].type == "response.output_item.added"
|
|
# The added item should be a ResponseFunctionToolCall.
|
|
added_fn = output_events[1].item
|
|
assert isinstance(added_fn, ResponseFunctionToolCall)
|
|
assert added_fn.name == "my_func" # Name should be complete from first chunk
|
|
assert added_fn.arguments == "" # Arguments start empty
|
|
assert output_events[2].type == "response.function_call_arguments.delta"
|
|
assert output_events[2].delta == "arg1" # First argument chunk
|
|
assert output_events[3].type == "response.function_call_arguments.delta"
|
|
assert output_events[3].delta == "arg2" # Second argument chunk
|
|
assert output_events[4].type == "response.output_item.done"
|
|
assert output_events[5].type == "response.completed"
|
|
# Final function call should have complete arguments
|
|
final_fn = output_events[4].item
|
|
assert isinstance(final_fn, ResponseFunctionToolCall)
|
|
assert final_fn.name == "my_func"
|
|
assert final_fn.arguments == "arg1arg2"
|
|
|
|
|
|
@pytest.mark.allow_call_model_methods
|
|
@pytest.mark.asyncio
|
|
async def test_stream_response_yields_real_time_function_call_arguments(monkeypatch) -> None:
|
|
"""
|
|
Validate that LiteLLM `stream_response` also emits function call arguments in real-time
|
|
as they are received, ensuring consistent behavior across model providers.
|
|
"""
|
|
# Simulate realistic chunks: name first, then arguments incrementally
|
|
tool_call_delta1 = ChoiceDeltaToolCall(
|
|
index=0,
|
|
id="litellm-call-456",
|
|
function=ChoiceDeltaToolCallFunction(name="generate_code", arguments=""),
|
|
type="function",
|
|
)
|
|
tool_call_delta2 = ChoiceDeltaToolCall(
|
|
index=0,
|
|
function=ChoiceDeltaToolCallFunction(arguments='{"language": "'),
|
|
type="function",
|
|
)
|
|
tool_call_delta3 = ChoiceDeltaToolCall(
|
|
index=0,
|
|
function=ChoiceDeltaToolCallFunction(arguments='python", "task": "'),
|
|
type="function",
|
|
)
|
|
tool_call_delta4 = ChoiceDeltaToolCall(
|
|
index=0,
|
|
function=ChoiceDeltaToolCallFunction(arguments='hello world"}'),
|
|
type="function",
|
|
)
|
|
|
|
chunk1 = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta1]))],
|
|
)
|
|
chunk2 = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta2]))],
|
|
)
|
|
chunk3 = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta3]))],
|
|
)
|
|
chunk4 = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta4]))],
|
|
usage=CompletionUsage(completion_tokens=1, prompt_tokens=1, total_tokens=2),
|
|
)
|
|
|
|
async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
|
|
for c in (chunk1, chunk2, chunk3, chunk4):
|
|
yield c
|
|
|
|
async def patched_fetch_response(self, *args, **kwargs):
|
|
resp = Response(
|
|
id="resp-id",
|
|
created_at=0,
|
|
model="fake-model",
|
|
object="response",
|
|
output=[],
|
|
tool_choice="none",
|
|
tools=[],
|
|
parallel_tool_calls=False,
|
|
)
|
|
return resp, fake_stream()
|
|
|
|
monkeypatch.setattr(LitellmModel, "_fetch_response", patched_fetch_response)
|
|
model = LitellmProvider().get_model("gpt-4")
|
|
output_events = []
|
|
async for event in model.stream_response(
|
|
system_instructions=None,
|
|
input="",
|
|
model_settings=ModelSettings(),
|
|
tools=[],
|
|
output_schema=None,
|
|
handoffs=[],
|
|
tracing=ModelTracing.DISABLED,
|
|
previous_response_id=None,
|
|
conversation_id=None,
|
|
prompt=None,
|
|
):
|
|
output_events.append(event)
|
|
|
|
# Extract events by type
|
|
function_args_delta_events = [
|
|
e for e in output_events if e.type == "response.function_call_arguments.delta"
|
|
]
|
|
output_item_added_events = [e for e in output_events if e.type == "response.output_item.added"]
|
|
|
|
# Verify we got real-time streaming (3 argument delta events)
|
|
assert len(function_args_delta_events) == 3
|
|
assert len(output_item_added_events) == 1
|
|
|
|
# Verify the deltas were streamed correctly
|
|
expected_deltas = ['{"language": "', 'python", "task": "', 'hello world"}']
|
|
for i, delta_event in enumerate(function_args_delta_events):
|
|
assert delta_event.delta == expected_deltas[i]
|
|
|
|
# Verify function call metadata
|
|
added_event = output_item_added_events[0]
|
|
assert isinstance(added_event.item, ResponseFunctionToolCall)
|
|
assert added_event.item.name == "generate_code"
|
|
assert added_event.item.call_id == "litellm-call-456"
|
|
|
|
|
|
@pytest.mark.allow_call_model_methods
|
|
@pytest.mark.asyncio
|
|
async def test_stream_response_synthesizes_refusal_on_content_filter(monkeypatch) -> None:
|
|
"""A stream that terminates with finish_reason == "content_filter" and no
|
|
emitted content (as Anthropic-on-Bedrock does via LiteLLM) must synthesize a
|
|
ResponseOutputRefusal so the completed response carries an explicit refusal
|
|
rather than an empty assistant turn.
|
|
|
|
Mirrors the real Bedrock chunk shape: an empty-string content delta followed
|
|
by a terminal content_filter chunk with no content. The empty "" delta must
|
|
not open a text content part; the synthesized refusal must be the only
|
|
content part, at the same index in the stream and in response.completed.
|
|
"""
|
|
chunk1 = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(role="assistant", content=""))],
|
|
)
|
|
chunk2 = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(), finish_reason="content_filter")],
|
|
usage=CompletionUsage(
|
|
completion_tokens=0,
|
|
prompt_tokens=7,
|
|
total_tokens=7,
|
|
),
|
|
)
|
|
|
|
async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
|
|
for c in (chunk1, chunk2):
|
|
yield c
|
|
|
|
async def patched_fetch_response(self, *args, **kwargs):
|
|
resp = Response(
|
|
id="resp-id",
|
|
created_at=0,
|
|
model="fake-model",
|
|
object="response",
|
|
output=[],
|
|
tool_choice="none",
|
|
tools=[],
|
|
parallel_tool_calls=False,
|
|
)
|
|
return resp, fake_stream()
|
|
|
|
monkeypatch.setattr(LitellmModel, "_fetch_response", patched_fetch_response)
|
|
model = LitellmProvider().get_model("gpt-4")
|
|
output_events = []
|
|
async for event in model.stream_response(
|
|
system_instructions=None,
|
|
input="",
|
|
model_settings=ModelSettings(),
|
|
tools=[],
|
|
output_schema=None,
|
|
handoffs=[],
|
|
tracing=ModelTracing.DISABLED,
|
|
previous_response_id=None,
|
|
conversation_id=None,
|
|
prompt=None,
|
|
):
|
|
output_events.append(event)
|
|
|
|
types = [e.type for e in output_events]
|
|
# Coherent refusal sequence: the message + refusal part are opened, a refusal
|
|
# delta is emitted, and the parts/message are closed before completion.
|
|
assert "response.output_item.added" in types
|
|
assert "response.content_part.added" in types
|
|
assert "response.refusal.delta" in types
|
|
assert types[-1] == "response.completed"
|
|
assert "response.output_item.done" in types
|
|
|
|
# The refusal delta carries a non-empty message.
|
|
refusal_deltas = [e for e in output_events if e.type == "response.refusal.delta"]
|
|
assert refusal_deltas and refusal_deltas[0].delta
|
|
|
|
# Event coherence: the assistant message is announced exactly once, and every
|
|
# content part that is opened is also closed.
|
|
assert types.count("response.output_item.added") == 1
|
|
assert types.count("response.content_part.added") == types.count("response.content_part.done")
|
|
|
|
# The empty "" content delta must NOT open a text content part: no text part
|
|
# events and no output_text.delta are emitted at all.
|
|
assert "response.output_text.delta" not in types
|
|
added_parts = [e for e in output_events if e.type == "response.content_part.added"]
|
|
assert len(added_parts) == 1
|
|
assert isinstance(added_parts[0].part, ResponseOutputRefusal)
|
|
|
|
# The completed response contains exactly one content part: the refusal.
|
|
completed_event = output_events[-1]
|
|
assert isinstance(completed_event, ResponseCompletedEvent)
|
|
completed_resp = completed_event.response
|
|
assert isinstance(completed_resp.output[0], ResponseOutputMessage)
|
|
assert len(completed_resp.output[0].content) == 1
|
|
refusal_part = completed_resp.output[0].content[0]
|
|
assert isinstance(refusal_part, ResponseOutputRefusal)
|
|
assert refusal_part.refusal
|
|
|
|
# The refusal's streamed content_index matches its position in the completed
|
|
# response (0), so raw-event replay and the final response stay aligned.
|
|
assert added_parts[0].content_index == 0
|
|
assert refusal_deltas[0].content_index == 0
|
|
done_parts = [e for e in output_events if e.type == "response.content_part.done"]
|
|
assert len(done_parts) == 1
|
|
assert done_parts[0].content_index == 0
|
|
|
|
|
|
@pytest.mark.allow_call_model_methods
|
|
@pytest.mark.asyncio
|
|
async def test_stream_response_content_filter_does_not_clobber_text(monkeypatch) -> None:
|
|
"""A content_filter finish_reason that arrives AFTER real text was streamed
|
|
must not synthesize a refusal (the text stands)."""
|
|
chunk1 = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(content="answer"))],
|
|
)
|
|
chunk2 = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(), finish_reason="content_filter")],
|
|
usage=CompletionUsage(completion_tokens=1, prompt_tokens=7, total_tokens=8),
|
|
)
|
|
|
|
async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
|
|
for c in (chunk1, chunk2):
|
|
yield c
|
|
|
|
async def patched_fetch_response(self, *args, **kwargs):
|
|
resp = Response(
|
|
id="resp-id",
|
|
created_at=0,
|
|
model="fake-model",
|
|
object="response",
|
|
output=[],
|
|
tool_choice="none",
|
|
tools=[],
|
|
parallel_tool_calls=False,
|
|
)
|
|
return resp, fake_stream()
|
|
|
|
monkeypatch.setattr(LitellmModel, "_fetch_response", patched_fetch_response)
|
|
model = LitellmProvider().get_model("gpt-4")
|
|
output_events = [
|
|
event
|
|
async for event in model.stream_response(
|
|
system_instructions=None,
|
|
input="",
|
|
model_settings=ModelSettings(),
|
|
tools=[],
|
|
output_schema=None,
|
|
handoffs=[],
|
|
tracing=ModelTracing.DISABLED,
|
|
previous_response_id=None,
|
|
conversation_id=None,
|
|
prompt=None,
|
|
)
|
|
]
|
|
|
|
assert "response.refusal.delta" not in [e.type for e in output_events]
|
|
completed_event = output_events[-1]
|
|
assert isinstance(completed_event, ResponseCompletedEvent)
|
|
completed_resp = completed_event.response
|
|
assert isinstance(completed_resp.output[0], ResponseOutputMessage)
|
|
assert isinstance(completed_resp.output[0].content[0], ResponseOutputText)
|
|
assert completed_resp.output[0].content[0].text == "answer"
|
|
|
|
|
|
@pytest.mark.allow_call_model_methods
|
|
@pytest.mark.asyncio
|
|
async def test_stream_response_content_filter_refusal_after_reasoning(monkeypatch) -> None:
|
|
"""A content_filter turn preceded by reasoning must still place the
|
|
synthesized refusal at content_index 0 of the assistant message. Reasoning
|
|
is a *separate* output item (it shifts the message's output_index, not its
|
|
content_index), so the refusal — the sole content part — stays at
|
|
content_index 0 in both the stream and response.completed."""
|
|
reasoning_delta = ChoiceDelta(role="assistant", content=None)
|
|
# reasoning_content is a provider extra field the handler reads via hasattr.
|
|
reasoning_delta.reasoning_content = "thinking..." # type: ignore[attr-defined]
|
|
chunk_reasoning = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=reasoning_delta)],
|
|
)
|
|
chunk_empty = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(content=""))],
|
|
)
|
|
chunk_filter = ChatCompletionChunk(
|
|
id="chunk-id",
|
|
created=1,
|
|
model="fake",
|
|
object="chat.completion.chunk",
|
|
choices=[Choice(index=0, delta=ChoiceDelta(), finish_reason="content_filter")],
|
|
usage=CompletionUsage(completion_tokens=0, prompt_tokens=7, total_tokens=7),
|
|
)
|
|
|
|
async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
|
|
for c in (chunk_reasoning, chunk_empty, chunk_filter):
|
|
yield c
|
|
|
|
async def patched_fetch_response(self, *args, **kwargs):
|
|
resp = Response(
|
|
id="resp-id",
|
|
created_at=0,
|
|
model="fake-model",
|
|
object="response",
|
|
output=[],
|
|
tool_choice="none",
|
|
tools=[],
|
|
parallel_tool_calls=False,
|
|
)
|
|
return resp, fake_stream()
|
|
|
|
monkeypatch.setattr(LitellmModel, "_fetch_response", patched_fetch_response)
|
|
model = LitellmProvider().get_model("gpt-4")
|
|
output_events = [
|
|
event
|
|
async for event in model.stream_response(
|
|
system_instructions=None,
|
|
input="",
|
|
model_settings=ModelSettings(),
|
|
tools=[],
|
|
output_schema=None,
|
|
handoffs=[],
|
|
tracing=ModelTracing.DISABLED,
|
|
previous_response_id=None,
|
|
conversation_id=None,
|
|
prompt=None,
|
|
)
|
|
]
|
|
|
|
# A reasoning item was produced as a separate output item.
|
|
completed_event = output_events[-1]
|
|
assert isinstance(completed_event, ResponseCompletedEvent)
|
|
completed_resp = completed_event.response
|
|
assert isinstance(completed_resp.output[0], ResponseReasoningItem)
|
|
assistant_msg = completed_resp.output[1]
|
|
assert isinstance(assistant_msg, ResponseOutputMessage)
|
|
# The refusal is the sole content part of the assistant message, at index 0.
|
|
assert len(assistant_msg.content) == 1
|
|
assert isinstance(assistant_msg.content[0], ResponseOutputRefusal)
|
|
|
|
# The assistant message's output_index is 1 (after the reasoning item), and
|
|
# every refusal event uses that output_index and content_index 0 — matching
|
|
# the refusal's position in response.completed.
|
|
added = [
|
|
e
|
|
for e in output_events
|
|
if isinstance(e, ResponseContentPartAddedEvent)
|
|
and isinstance(e.part, ResponseOutputRefusal)
|
|
]
|
|
deltas = [e for e in output_events if isinstance(e, ResponseRefusalDeltaEvent)]
|
|
assert len(added) == 1
|
|
assert added[0].content_index == 0
|
|
assert added[0].output_index == 1
|
|
assert deltas and all(d.content_index == 0 and d.output_index == 1 for d in deltas)
|
|
# The empty "" delta still opens no text part.
|
|
assert "response.output_text.delta" not in [e.type for e in output_events]
|