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4cb2ecd948
Two correctness fixes uncovered while landing the OTel finish_reason +
response_id plumbing:
- LiteLLM streaming (sync + async): `stream_options={"include_usage": True}`
causes LiteLLM to emit a final usage-only chunk with `choices=[]`. The
post-loop `_extract_finish_reason_and_response_id(last_chunk)` silently
returned `(None, None)` because the last chunk has no choices, even though
earlier chunks carried `finish_reason="stop"`. Track both fields
incrementally inside the loop (mirroring how OpenAI/Gemini/Azure already
handle their native streams) and use the tracked values for the
LLMCallCompletedEvent emission and the partial-response error path.
- Bedrock Converse: `ResponseMetadata.RequestId` is an AWS infra trace id,
not a model-level response id (semantically different from OpenAI's
`chatcmpl-XXX`). Return None for `response_id` rather than mislead
downstream telemetry consumers. The audit-fix's async propagation chain
still works — None propagates through unchanged.
Adds `test_llm_streaming_finish_reason.py` pinning both the sync and async
LiteLLM streaming paths against the include_usage chunk shape.
97 lines
2.9 KiB
Python
97 lines
2.9 KiB
Python
"""Regression: LiteLLM emits a final usage-only chunk (choices=[]) when
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``stream_options.include_usage`` is set. The old post-loop
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``_extract_finish_reason_and_response_id(last_chunk)`` then silently returned
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(None, None). These tests pin that we capture finish_reason/response_id
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incrementally during the stream loop instead.
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"""
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from __future__ import annotations
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from typing import Any
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from unittest.mock import patch
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import pytest
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from crewai.events.event_bus import CrewAIEventsBus
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from crewai.events.types.llm_events import LLMCallCompletedEvent
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from crewai.llm import LLM
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@pytest.fixture
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def mock_emit():
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with patch.object(CrewAIEventsBus, "emit") as mock:
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yield mock
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def _completed_event(mock_emit) -> LLMCallCompletedEvent:
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matches = [
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call.kwargs["event"]
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for call in mock_emit.call_args_list
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if isinstance(call.kwargs.get("event"), LLMCallCompletedEvent)
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]
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assert matches, "expected an LLMCallCompletedEvent to be emitted"
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assert len(matches) == 1, f"expected one completed event, got {len(matches)}"
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return matches[0]
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def _chunks_with_usage_tail() -> list[dict[str, Any]]:
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"""Three-chunk stream mirroring LiteLLM's include_usage behavior:
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two content chunks where the second carries finish_reason="stop",
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then a final usage-only chunk with choices=[]."""
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return [
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{
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"id": "chatcmpl-stream-1",
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"choices": [
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{"delta": {"content": "hi"}, "finish_reason": None}
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],
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},
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{
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"id": "chatcmpl-stream-1",
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"choices": [
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{"delta": {"content": " there"}, "finish_reason": "stop"}
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],
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},
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{
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"id": "chatcmpl-stream-1",
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"choices": [],
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"usage": {
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"prompt_tokens": 1,
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"completion_tokens": 2,
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"total_tokens": 3,
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},
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},
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]
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def test_sync_stream_emits_finish_reason_and_response_id_from_loop(mock_emit):
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llm = LLM(model="gpt-4o-mini", is_litellm=True, stream=True)
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with patch("crewai.llm.litellm.completion", return_value=iter(_chunks_with_usage_tail())):
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result = llm.call("anything")
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assert result == "hi there"
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event = _completed_event(mock_emit)
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assert event.finish_reason == "stop"
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assert event.response_id == "chatcmpl-stream-1"
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@pytest.mark.asyncio
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async def test_async_stream_emits_finish_reason_and_response_id_from_loop(mock_emit):
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llm = LLM(model="gpt-4o-mini", is_litellm=True, stream=True)
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async def _aiter():
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for chunk in _chunks_with_usage_tail():
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yield chunk
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async def _acompletion(*_args, **_kwargs):
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return _aiter()
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with patch("crewai.llm.litellm.acompletion", side_effect=_acompletion):
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result = await llm.acall("anything")
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assert result == "hi there"
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event = _completed_event(mock_emit)
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assert event.finish_reason == "stop"
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assert event.response_id == "chatcmpl-stream-1"
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