wehub-resource-sync
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861 lines
32 KiB
Python
861 lines
32 KiB
Python
# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
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#
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# SPDX-License-Identifier: Apache-2.0
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from unittest.mock import call, patch
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import pytest
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from openai.types.chat import chat_completion_chunk
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from haystack.components.generators.utils import (
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_convert_streaming_chunks_to_chat_message,
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_normalize_messages,
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print_streaming_chunk,
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)
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from haystack.dataclasses import (
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ChatMessage,
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ComponentInfo,
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ReasoningContent,
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StreamingChunk,
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ToolCall,
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ToolCallDelta,
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ToolCallResult,
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)
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def test_convert_streaming_chunks_to_chat_message_tool_calls_in_any_chunk():
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chunks = [
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": None,
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.910076",
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},
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component_info=ComponentInfo(name="test", type="test"),
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=0,
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id="call_ZOj5l67zhZOx6jqjg7ATQwb6",
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function=chat_completion_chunk.ChoiceDeltaToolCallFunction(
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arguments="", name="rag_pipeline_tool"
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),
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type="function",
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)
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],
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.913919",
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},
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component_info=ComponentInfo(name="test", type="test"),
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index=0,
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start=True,
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tool_calls=[
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ToolCallDelta(id="call_ZOj5l67zhZOx6jqjg7ATQwb6", tool_name="rag_pipeline_tool", arguments="", index=0)
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],
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=0, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments='{"qu')
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)
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],
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.914439",
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},
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component_info=ComponentInfo(name="test", type="test"),
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index=0,
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tool_calls=[ToolCallDelta(arguments='{"qu', index=0)],
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=0, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments='ery":')
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)
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],
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.924146",
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},
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component_info=ComponentInfo(name="test", type="test"),
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index=0,
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tool_calls=[ToolCallDelta(arguments='ery":', index=0)],
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=0, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments=' "Wher')
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)
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],
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.924420",
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},
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component_info=ComponentInfo(name="test", type="test"),
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index=0,
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tool_calls=[ToolCallDelta(arguments=' "Wher', index=0)],
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=0, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments="e do")
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)
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],
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.944398",
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},
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component_info=ComponentInfo(name="test", type="test"),
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index=0,
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tool_calls=[ToolCallDelta(arguments="e do", index=0)],
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=0, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments="es Ma")
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)
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],
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.944958",
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},
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component_info=ComponentInfo(name="test", type="test"),
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index=0,
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tool_calls=[ToolCallDelta(arguments="es Ma", index=0)],
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=0, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments="rk liv")
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)
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],
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.945507",
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},
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component_info=ComponentInfo(name="test", type="test"),
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index=0,
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tool_calls=[ToolCallDelta(arguments="rk liv", index=0)],
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=0, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments='e?"}')
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)
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],
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.946018",
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},
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component_info=ComponentInfo(name="test", type="test"),
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index=0,
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tool_calls=[ToolCallDelta(arguments='e?"}', index=0)],
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=1,
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id="call_STxsYY69wVOvxWqopAt3uWTB",
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function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments="", name="get_weather"),
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type="function",
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)
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],
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.946578",
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},
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component_info=ComponentInfo(name="test", type="test"),
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index=1,
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start=True,
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tool_calls=[
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ToolCallDelta(id="call_STxsYY69wVOvxWqopAt3uWTB", tool_name="get_weather", arguments="", index=1)
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],
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=1, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments='{"ci')
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)
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],
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.946981",
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},
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component_info=ComponentInfo(name="test", type="test"),
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index=1,
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tool_calls=[ToolCallDelta(arguments='{"ci', index=1)],
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=1, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments='ty": ')
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)
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],
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.947411",
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},
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component_info=ComponentInfo(name="test", type="test"),
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index=1,
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tool_calls=[ToolCallDelta(arguments='ty": ', index=1)],
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=1, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments='"Berli')
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)
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],
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.947643",
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},
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component_info=ComponentInfo(name="test", type="test"),
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index=1,
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tool_calls=[ToolCallDelta(arguments='"Berli', index=1)],
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=1, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments='n"}')
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)
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],
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.947939",
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},
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component_info=ComponentInfo(name="test", type="test"),
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index=1,
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tool_calls=[ToolCallDelta(arguments='n"}', index=1)],
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": None,
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"finish_reason": "tool_calls",
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"received_at": "2025-02-19T16:02:55.948772",
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},
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component_info=ComponentInfo(name="test", type="test"),
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finish_reason="tool_calls",
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": None,
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.948772",
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"usage": {
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"completion_tokens": 42,
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"prompt_tokens": 282,
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"total_tokens": 324,
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"completion_tokens_details": {
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"accepted_prediction_tokens": 0,
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"audio_tokens": 0,
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"reasoning_tokens": 0,
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"rejected_prediction_tokens": 0,
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},
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"prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0},
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},
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},
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component_info=ComponentInfo(name="test", type="test"),
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),
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]
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# Convert chunks to a chat message
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result = _convert_streaming_chunks_to_chat_message(chunks=chunks)
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assert not result.texts
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assert not result.text
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# Verify both tool calls were found and processed
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assert len(result.tool_calls) == 2
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assert result.tool_calls[0].id == "call_ZOj5l67zhZOx6jqjg7ATQwb6"
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assert result.tool_calls[0].tool_name == "rag_pipeline_tool"
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assert result.tool_calls[0].arguments == {"query": "Where does Mark live?"}
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assert result.tool_calls[1].id == "call_STxsYY69wVOvxWqopAt3uWTB"
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assert result.tool_calls[1].tool_name == "get_weather"
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assert result.tool_calls[1].arguments == {"city": "Berlin"}
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# Verify meta information
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assert result.meta["model"] == "gpt-4o-mini-2024-07-18"
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assert result.meta["finish_reason"] == "tool_calls"
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assert result.meta["index"] == 0
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assert result.meta["completion_start_time"] == "2025-02-19T16:02:55.910076"
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assert result.meta["usage"] == {
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"completion_tokens": 42,
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"prompt_tokens": 282,
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"total_tokens": 324,
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"completion_tokens_details": {
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"accepted_prediction_tokens": 0,
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"audio_tokens": 0,
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"reasoning_tokens": 0,
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"rejected_prediction_tokens": 0,
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},
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"prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0},
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}
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def test_convert_streaming_chunk_to_chat_message_two_tool_calls_in_same_chunk():
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chunks = [
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StreamingChunk(
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content="",
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meta={
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"model": "mistral-small-latest",
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"index": 0,
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"tool_calls": None,
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"finish_reason": None,
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"usage": None,
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},
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component_info=ComponentInfo(
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type="haystack_integrations.components.generators.mistral.chat.chat_generator.MistralChatGenerator",
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name=None,
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),
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),
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StreamingChunk(
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content="",
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meta={
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"model": "mistral-small-latest",
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"index": 0,
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"finish_reason": "tool_calls",
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"usage": {
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"completion_tokens": 35,
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"prompt_tokens": 77,
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"total_tokens": 112,
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"completion_tokens_details": None,
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"prompt_tokens_details": None,
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},
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},
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component_info=ComponentInfo(
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type="haystack_integrations.components.generators.mistral.chat.chat_generator.MistralChatGenerator",
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name=None,
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),
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index=0,
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tool_calls=[
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ToolCallDelta(index=0, tool_name="weather", arguments='{"city": "Paris"}', id="FL1FFlqUG"),
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ToolCallDelta(index=1, tool_name="weather", arguments='{"city": "Berlin"}', id="xSuhp66iB"),
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],
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start=True,
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finish_reason="tool_calls",
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),
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]
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# Convert chunks to a chat message
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result = _convert_streaming_chunks_to_chat_message(chunks=chunks)
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assert not result.texts
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assert not result.text
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# Verify both tool calls were found and processed
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assert len(result.tool_calls) == 2
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assert result.tool_calls[0].id == "FL1FFlqUG"
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assert result.tool_calls[0].tool_name == "weather"
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assert result.tool_calls[0].arguments == {"city": "Paris"}
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assert result.tool_calls[1].id == "xSuhp66iB"
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assert result.tool_calls[1].tool_name == "weather"
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assert result.tool_calls[1].arguments == {"city": "Berlin"}
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|
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def test_convert_streaming_chunk_to_chat_message_empty_tool_call_delta():
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chunks = [
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
|
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"index": 0,
|
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"tool_calls": None,
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.910076",
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},
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component_info=ComponentInfo(name="test", type="test"),
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
|
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"index": 0,
|
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=0,
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id="call_ZOj5l67zhZOx6jqjg7ATQwb6",
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function=chat_completion_chunk.ChoiceDeltaToolCallFunction(
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arguments='{"query":', name="rag_pipeline_tool"
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),
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type="function",
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)
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],
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.913919",
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},
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component_info=ComponentInfo(name="test", type="test"),
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index=0,
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start=True,
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tool_calls=[
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ToolCallDelta(
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id="call_ZOj5l67zhZOx6jqjg7ATQwb6", tool_name="rag_pipeline_tool", arguments='{"query":', index=0
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)
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],
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
|
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"index": 0,
|
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=0,
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function=chat_completion_chunk.ChoiceDeltaToolCallFunction(
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arguments=' "Where does Mark live?"}'
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),
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)
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],
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"finish_reason": None,
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"received_at": "2025-02-19T16:02:55.924420",
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},
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component_info=ComponentInfo(name="test", type="test"),
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index=0,
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tool_calls=[ToolCallDelta(arguments=' "Where does Mark live?"}', index=0)],
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),
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StreamingChunk(
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content="",
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meta={
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"model": "gpt-4o-mini-2024-07-18",
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"index": 0,
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"tool_calls": [
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=0, function=chat_completion_chunk.ChoiceDeltaToolCallFunction()
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)
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],
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"finish_reason": "tool_calls",
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"received_at": "2025-02-19T16:02:55.948772",
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},
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tool_calls=[ToolCallDelta(index=0)],
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component_info=ComponentInfo(name="test", type="test"),
|
|
finish_reason="tool_calls",
|
|
index=0,
|
|
),
|
|
StreamingChunk(
|
|
content="",
|
|
meta={
|
|
"model": "gpt-4o-mini-2024-07-18",
|
|
"index": 0,
|
|
"tool_calls": None,
|
|
"finish_reason": None,
|
|
"received_at": "2025-02-19T16:02:55.948772",
|
|
"usage": {
|
|
"completion_tokens": 42,
|
|
"prompt_tokens": 282,
|
|
"total_tokens": 324,
|
|
"completion_tokens_details": {
|
|
"accepted_prediction_tokens": 0,
|
|
"audio_tokens": 0,
|
|
"reasoning_tokens": 0,
|
|
"rejected_prediction_tokens": 0,
|
|
},
|
|
"prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0},
|
|
},
|
|
},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
),
|
|
]
|
|
|
|
# Convert chunks to a chat message
|
|
result = _convert_streaming_chunks_to_chat_message(chunks=chunks)
|
|
|
|
assert not result.texts
|
|
assert not result.text
|
|
|
|
# Verify both tool calls were found and processed
|
|
assert len(result.tool_calls) == 1
|
|
assert result.tool_calls[0].id == "call_ZOj5l67zhZOx6jqjg7ATQwb6"
|
|
assert result.tool_calls[0].tool_name == "rag_pipeline_tool"
|
|
assert result.tool_calls[0].arguments == {"query": "Where does Mark live?"}
|
|
assert result.meta["finish_reason"] == "tool_calls"
|
|
|
|
|
|
def test_convert_streaming_chunk_to_chat_message_with_empty_tool_call_arguments():
|
|
chunks = [
|
|
# Message start with input tokens
|
|
StreamingChunk(
|
|
content="",
|
|
meta={
|
|
"type": "message_start",
|
|
"message": {
|
|
"id": "msg_123",
|
|
"type": "message",
|
|
"role": "assistant",
|
|
"content": [],
|
|
"model": "claude-sonnet-4-20250514",
|
|
"stop_reason": None,
|
|
"stop_sequence": None,
|
|
"usage": {"input_tokens": 25, "output_tokens": 0},
|
|
},
|
|
},
|
|
index=0,
|
|
tool_calls=[],
|
|
tool_call_result=None,
|
|
start=True,
|
|
finish_reason=None,
|
|
),
|
|
# Initial text content
|
|
StreamingChunk(
|
|
content="",
|
|
meta={"type": "content_block_start", "index": 0, "content_block": {"type": "text", "text": ""}},
|
|
index=1,
|
|
tool_calls=[],
|
|
tool_call_result=None,
|
|
start=True,
|
|
finish_reason=None,
|
|
),
|
|
StreamingChunk(
|
|
content="Let me check",
|
|
meta={"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "Let me check"}},
|
|
index=2,
|
|
tool_calls=[],
|
|
tool_call_result=None,
|
|
start=False,
|
|
finish_reason=None,
|
|
),
|
|
StreamingChunk(
|
|
content=" the weather",
|
|
meta={"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": " the weather"}},
|
|
index=3,
|
|
tool_calls=[],
|
|
tool_call_result=None,
|
|
start=False,
|
|
finish_reason=None,
|
|
),
|
|
# Tool use content
|
|
StreamingChunk(
|
|
content="",
|
|
meta={
|
|
"type": "content_block_start",
|
|
"index": 1,
|
|
"content_block": {"type": "tool_use", "id": "toolu_123", "name": "weather", "input": {}},
|
|
},
|
|
index=5,
|
|
tool_calls=[ToolCallDelta(index=1, id="toolu_123", tool_name="weather", arguments=None)],
|
|
tool_call_result=None,
|
|
start=True,
|
|
finish_reason=None,
|
|
),
|
|
StreamingChunk(
|
|
content="",
|
|
meta={"type": "content_block_delta", "index": 1, "delta": {"type": "input_json_delta", "partial_json": ""}},
|
|
index=7,
|
|
tool_calls=[ToolCallDelta(index=1, id=None, tool_name=None, arguments="")],
|
|
tool_call_result=None,
|
|
start=False,
|
|
finish_reason=None,
|
|
),
|
|
# Final message delta
|
|
StreamingChunk(
|
|
content="",
|
|
meta={
|
|
"type": "message_delta",
|
|
"delta": {"stop_reason": "tool_use", "stop_sequence": None},
|
|
"usage": {"completion_tokens": 40},
|
|
},
|
|
index=8,
|
|
tool_calls=[],
|
|
tool_call_result=None,
|
|
start=False,
|
|
finish_reason="tool_calls",
|
|
),
|
|
]
|
|
|
|
message = _convert_streaming_chunks_to_chat_message(chunks=chunks)
|
|
|
|
assert message.texts == ["Let me check the weather"]
|
|
assert len(message.tool_calls) == 1
|
|
assert message.tool_calls[0].arguments == {}
|
|
assert message.tool_calls[0].id == "toolu_123"
|
|
assert message.tool_calls[0].tool_name == "weather"
|
|
|
|
|
|
def test_print_streaming_chunk_content_only():
|
|
chunk = StreamingChunk(
|
|
content="Hello, world!",
|
|
meta={"model": "test-model"},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
start=True,
|
|
)
|
|
with patch("builtins.print") as mock_print:
|
|
print_streaming_chunk(chunk)
|
|
expected_calls = [call("[ASSISTANT]\n", flush=True, end=""), call("Hello, world!", flush=True, end="")]
|
|
mock_print.assert_has_calls(expected_calls)
|
|
|
|
|
|
def test_print_streaming_chunk_tool_call():
|
|
chunk = StreamingChunk(
|
|
content="",
|
|
meta={"model": "test-model"},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
start=True,
|
|
index=0,
|
|
tool_calls=[ToolCallDelta(id="call_123", tool_name="test_tool", arguments='{"param": "value"}', index=0)],
|
|
)
|
|
with patch("builtins.print") as mock_print:
|
|
print_streaming_chunk(chunk)
|
|
expected_calls = [
|
|
call("[TOOL CALL]\nTool: test_tool \nArguments: ", flush=True, end=""),
|
|
call('{"param": "value"}', flush=True, end=""),
|
|
]
|
|
mock_print.assert_has_calls(expected_calls)
|
|
|
|
|
|
def test_print_streaming_chunk_tool_call_result():
|
|
chunk = StreamingChunk(
|
|
content="",
|
|
meta={"model": "test-model"},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
index=0,
|
|
tool_call_result=ToolCallResult(
|
|
result="Tool execution completed successfully",
|
|
origin=ToolCall(id="call_123", tool_name="test_tool", arguments={}),
|
|
error=False,
|
|
),
|
|
)
|
|
with patch("builtins.print") as mock_print:
|
|
print_streaming_chunk(chunk)
|
|
expected_calls = [call("[TOOL RESULT]\nTool execution completed successfully", flush=True, end="")]
|
|
mock_print.assert_has_calls(expected_calls)
|
|
|
|
|
|
def test_print_streaming_chunk_with_finish_reason():
|
|
chunk = StreamingChunk(
|
|
content="Final content.",
|
|
meta={"model": "test-model"},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
start=True,
|
|
finish_reason="stop",
|
|
)
|
|
with patch("builtins.print") as mock_print:
|
|
print_streaming_chunk(chunk)
|
|
expected_calls = [
|
|
call("[ASSISTANT]\n", flush=True, end=""),
|
|
call("Final content.", flush=True, end=""),
|
|
call("\n\n", flush=True, end=""),
|
|
]
|
|
mock_print.assert_has_calls(expected_calls)
|
|
|
|
|
|
def test_print_streaming_chunk_empty_chunk():
|
|
chunk = StreamingChunk(
|
|
content="", meta={"model": "test-model"}, component_info=ComponentInfo(name="test", type="test")
|
|
)
|
|
with patch("builtins.print") as mock_print:
|
|
print_streaming_chunk(chunk)
|
|
mock_print.assert_not_called()
|
|
|
|
|
|
def test_convert_streaming_chunks_to_chat_message_usage_not_in_last_chunk():
|
|
"""
|
|
Test that usage info is correctly extracted even when it's not in the last chunk.
|
|
This can happen with some API providers like Qwen3 where usage info may be returned
|
|
in a different chunk than the final one.
|
|
"""
|
|
chunks = [
|
|
StreamingChunk(
|
|
content="",
|
|
meta={"model": "qwen-plus", "index": 0, "finish_reason": None, "received_at": "2025-01-01T00:00:00.000000"},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
),
|
|
StreamingChunk(
|
|
content="Hello",
|
|
meta={"model": "qwen-plus", "index": 0, "finish_reason": None, "received_at": "2025-01-01T00:00:00.100000"},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
index=0,
|
|
start=True,
|
|
),
|
|
StreamingChunk(
|
|
content=" world",
|
|
meta={"model": "qwen-plus", "index": 0, "finish_reason": None, "received_at": "2025-01-01T00:00:00.200000"},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
index=0,
|
|
),
|
|
# Chunk with usage info (not the last chunk)
|
|
StreamingChunk(
|
|
content="",
|
|
meta={
|
|
"model": "qwen-plus",
|
|
"received_at": "2025-01-01T00:00:00.300000",
|
|
"usage": {"completion_tokens": 10, "prompt_tokens": 20, "total_tokens": 30},
|
|
},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
index=None,
|
|
),
|
|
# Final chunk with finish_reason but no usage (simulating Qwen3 behavior)
|
|
StreamingChunk(
|
|
content="",
|
|
meta={
|
|
"model": "qwen-plus",
|
|
"index": 0,
|
|
"finish_reason": "stop",
|
|
"received_at": "2025-01-01T00:00:00.400000",
|
|
"usage": None, # No usage info in final chunk
|
|
},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
finish_reason="stop",
|
|
),
|
|
]
|
|
|
|
result = _convert_streaming_chunks_to_chat_message(chunks=chunks)
|
|
|
|
assert result.text == "Hello world"
|
|
assert result.meta["model"] == "qwen-plus"
|
|
assert result.meta["finish_reason"] == "stop"
|
|
# Usage should be extracted from the chunk that has it, not just the last chunk
|
|
assert result.meta["usage"] == {"completion_tokens": 10, "prompt_tokens": 20, "total_tokens": 30}
|
|
|
|
|
|
def test_convert_streaming_chunks_to_chat_message_with_reasoning():
|
|
"""Test that reasoning content is correctly accumulated from streaming chunks."""
|
|
chunks = [
|
|
StreamingChunk(
|
|
content="",
|
|
meta={"model": "test-model", "received_at": "2025-01-01T00:00:00"},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
reasoning=ReasoningContent(reasoning_text="Let me think about this..."),
|
|
index=0,
|
|
),
|
|
StreamingChunk(
|
|
content="",
|
|
meta={"model": "test-model", "received_at": "2025-01-01T00:00:01"},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
reasoning=ReasoningContent(reasoning_text=" The capital of France is Paris."),
|
|
index=0,
|
|
),
|
|
StreamingChunk(
|
|
content="Paris",
|
|
meta={"model": "test-model", "received_at": "2025-01-01T00:00:02"},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
),
|
|
StreamingChunk(
|
|
content="",
|
|
meta={"model": "test-model", "received_at": "2025-01-01T00:00:03"},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
finish_reason="stop",
|
|
),
|
|
]
|
|
|
|
result = _convert_streaming_chunks_to_chat_message(chunks=chunks)
|
|
|
|
assert result.text == "Paris"
|
|
assert result.reasoning is not None
|
|
assert isinstance(result.reasoning, ReasoningContent)
|
|
assert result.reasoning.reasoning_text == "Let me think about this... The capital of France is Paris."
|
|
assert result.meta["finish_reason"] == "stop"
|
|
|
|
|
|
def test_convert_streaming_chunks_to_chat_message_without_reasoning():
|
|
"""Test that messages without reasoning work correctly (backward compatibility)."""
|
|
chunks = [
|
|
StreamingChunk(
|
|
content="Hello",
|
|
meta={"model": "test-model", "received_at": "2025-01-01T00:00:00"},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
),
|
|
StreamingChunk(
|
|
content=" world",
|
|
meta={"model": "test-model", "received_at": "2025-01-01T00:00:01"},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
finish_reason="stop",
|
|
),
|
|
]
|
|
|
|
result = _convert_streaming_chunks_to_chat_message(chunks=chunks)
|
|
|
|
assert result.text == "Hello world"
|
|
assert result.reasoning is None
|
|
|
|
|
|
def test_print_streaming_chunk_with_reasoning():
|
|
"""Test that print_streaming_chunk handles reasoning content correctly."""
|
|
chunk = StreamingChunk(
|
|
content="",
|
|
meta={"model": "test-model"},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
start=True,
|
|
reasoning=ReasoningContent(reasoning_text="I am thinking about this question."),
|
|
index=0,
|
|
)
|
|
with patch("builtins.print") as mock_print:
|
|
print_streaming_chunk(chunk)
|
|
expected_calls = [
|
|
call("[REASONING]\n", flush=True, end=""),
|
|
call("I am thinking about this question.", flush=True, end=""),
|
|
]
|
|
mock_print.assert_has_calls(expected_calls)
|
|
|
|
|
|
def test_print_streaming_chunk_with_reasoning_continuation():
|
|
"""Test that print_streaming_chunk handles reasoning continuation correctly."""
|
|
chunk = StreamingChunk(
|
|
content="",
|
|
meta={"model": "test-model"},
|
|
component_info=ComponentInfo(name="test", type="test"),
|
|
start=False, # Not the first chunk
|
|
reasoning=ReasoningContent(reasoning_text="continued reasoning..."),
|
|
index=0,
|
|
)
|
|
with patch("builtins.print") as mock_print:
|
|
print_streaming_chunk(chunk)
|
|
# Should only print the reasoning text without the header since it's a continuation
|
|
expected_calls = [call("continued reasoning...", flush=True, end="")]
|
|
mock_print.assert_has_calls(expected_calls)
|
|
|
|
|
|
def test_normalize_messages():
|
|
assert _normalize_messages("Hello") == [ChatMessage.from_user("Hello")]
|
|
assert _normalize_messages([ChatMessage.from_user("World")]) == [ChatMessage.from_user("World")]
|
|
|
|
with pytest.raises(TypeError):
|
|
_normalize_messages(123)
|