mkbk
357 lines
16 KiB
Python
357 lines
16 KiB
Python
from __future__ import annotations
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import tempfile
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import unittest
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from pathlib import Path
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from owner_voice_pet.agent_memory import FaissIndexManifest, FakeMemoryManager, MemoryRecordInput, SQLiteMemoryManager
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from owner_voice_pet.config import AppConfig
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from owner_voice_pet.full_duplex_audio import FakeWebRtcAudioProcessingProvider, RenderReferenceRingBuffer
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from owner_voice_pet.full_duplex_control import CancellationGraph, FullDuplexStateMachine
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from owner_voice_pet.full_duplex_response import (
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FakeStreamingLlmProvider,
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FakeStreamingTtsProvider,
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InterruptiblePlaybackQueue,
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LlmStreamEvent,
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SentenceSegmenter,
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)
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from owner_voice_pet.full_duplex_speech import FakeStreamingSttProvider, FakeVadProvider, InterruptionDetector, TranscriptEvent
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from owner_voice_pet.full_duplex_runtime import FullDuplexAgentRuntime
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from owner_voice_pet.full_duplex_testing import (
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PerformanceMetricRecorder,
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build_fake_full_duplex_audio_fixture,
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diagnostics_contain_sensitive_data,
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sanitize_diagnostics,
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)
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from owner_voice_pet.llm import MockLlmProvider
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from owner_voice_pet.models import AudioFrame, Message, PipelineState
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from owner_voice_pet.stt import MetadataSttProvider
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from owner_voice_pet.tool_router import MemorySearchTool, ToolCallRequest, ToolContext, ToolRouter
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from owner_voice_pet.transport import MemoryAudioTransport
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from owner_voice_pet.tts import SineTtsProvider
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from owner_voice_pet.vad import EnergyVadProvider, VadRecorder
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class FullDuplexIntegrationTests(unittest.TestCase):
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def test_run_agent_live_runtime_once_consumes_audio_and_replies(self) -> None:
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frames = [
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AudioFrame(b"\xff\x7f", 16000, 1, 0, 1, {"duration_ms": 20, "speech": True, "transcript": "直接提问"}),
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AudioFrame(b"\xff\x7f", 16000, 1, 20, 2, {"duration_ms": 20, "speech": True, "transcript": "直接提问"}),
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AudioFrame(b"\x00\x00", 16000, 1, 40, 3, {"duration_ms": 20, "speech": False, "transcript": "直接提问"}),
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AudioFrame(b"\x00\x00", 16000, 1, 60, 4, {"duration_ms": 20, "speech": False, "transcript": "直接提问"}),
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]
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transport = MemoryAudioTransport(frames, flush_clears_input=False)
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runtime = FullDuplexAgentRuntime(
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config=AppConfig(
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audio_apm_provider="fake",
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audio_apm_required=False,
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memory_enabled=False,
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tool_router_enabled=False,
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vad_min_duration_ms=40,
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vad_end_silence_ms=40,
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end_chime_enabled=False,
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),
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transport=transport,
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vad_recorder=VadRecorder(EnergyVadProvider(), min_duration_ms=40, end_silence_ms=40),
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stt=MetadataSttProvider(),
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llm=MockLlmProvider(["这是全双工回答。"]),
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tts=SineTtsProvider(),
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)
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summary = runtime.run(once=True)
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self.assertEqual(summary.completed_turns, 1)
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self.assertEqual(summary.failed_turns, 0)
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self.assertEqual([message.role for message in runtime.context.messages()], ["user", "assistant"])
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self.assertEqual(runtime.context.messages()[0].content, "直接提问")
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self.assertEqual(runtime.context.messages()[1].content, "这是全双工回答。")
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self.assertGreaterEqual(len(transport.played_segments), 1)
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self.assertIsNotNone(runtime.audio_hub)
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self.assertGreater(runtime.audio_hub.processed_capture.frame_count, 0)
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def test_fake_apm_echo_does_not_trigger_interruption(self) -> None:
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fixture = build_fake_full_duplex_audio_fixture()
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apm = FakeWebRtcAudioProcessingProvider()
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detector = InterruptionDetector(vad=FakeVadProvider(), min_speech_ms=20)
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render = fixture[0]
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apm.process_render(render)
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processed_echo = apm.process_capture(fixture[0])
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decision = detector.accept(
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processed_echo,
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state=PipelineState.SPEAKING,
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stt_events=[TranscriptEvent("stable_partial", "助手", is_stable=True)],
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)
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self.assertTrue(processed_echo.metadata["echo_suppressed"])
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self.assertFalse(decision.interrupted)
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def test_speaking_interruption_cancels_response_and_returns_to_listening(self) -> None:
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machine = FullDuplexStateMachine()
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graph = CancellationGraph("turn")
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detector = InterruptionDetector(vad=FakeVadProvider(), min_speech_ms=200)
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machine.transition(PipelineState.LISTENING, event_type="start")
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machine.transition(PipelineState.THINKING, event_type="final_transcript")
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machine.transition(PipelineState.SPEAKING, event_type="first_tts_chunk")
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detector.accept(
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build_fake_full_duplex_audio_fixture()[1],
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state=PipelineState.SPEAKING,
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stt_events=[TranscriptEvent("partial", "你", is_stable=False)],
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)
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decision = detector.accept(
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build_fake_full_duplex_audio_fixture()[2],
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state=PipelineState.SPEAKING,
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stt_events=[TranscriptEvent("stable_partial", "你好", is_stable=True)],
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)
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if decision.interrupted:
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graph.cancel_all("user interrupted")
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machine.transition(PipelineState.INTERRUPTED, event_type="interrupt_detected")
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machine.transition(PipelineState.LISTENING, event_type="buffered_user_audio")
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self.assertTrue(decision.interrupted)
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self.assertTrue(graph.root.cancelled)
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self.assertEqual(machine.current_state, PipelineState.LISTENING)
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def test_full_duplex_runtime_interrupt_fixture_uses_audio_hub_and_buffers_user_audio(self) -> None:
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runtime = FullDuplexAgentRuntime(
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config=AppConfig(
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audio_apm_provider="fake",
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audio_apm_required=False,
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barge_in_min_speech_ms=200,
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)
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)
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fixture = build_fake_full_duplex_audio_fixture()[1:3]
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summary = runtime.run_interrupt_fixture(fixture, initial_state=PipelineState.SPEAKING)
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self.assertTrue(summary.interrupted)
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self.assertTrue(runtime.cancellation_graph.root.cancelled)
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self.assertEqual(runtime.state_machine.current_state, PipelineState.LISTENING)
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self.assertIsNotNone(runtime.interrupt_controller)
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self.assertEqual(
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[item.frame_id for item in runtime.interrupt_controller.buffered_user_frames],
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[2, 3],
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)
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def test_streaming_stt_llm_tts_playback_order(self) -> None:
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stt = FakeStreamingSttProvider(
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scripted_events=[
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[TranscriptEvent("partial", "你", is_stable=False)],
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[TranscriptEvent("stable_partial", "你好", is_stable=True)],
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],
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final_text="你好",
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)
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stt_session = stt.start_session("turn")
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llm = FakeStreamingLlmProvider([LlmStreamEvent("delta", "你好。"), LlmStreamEvent("finish", finish_reason="stop")])
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segmenter = SentenceSegmenter()
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tts_session = FakeStreamingTtsProvider().start_stream(voice="default", sample_rate=16000)
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playback = InterruptiblePlaybackQueue()
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render = RenderReferenceRingBuffer(capacity_ms=1000)
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graph = CancellationGraph("turn")
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event_order: list[str] = []
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for frame in build_fake_full_duplex_audio_fixture()[1:3]:
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for event in stt_session.accept_audio(frame):
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event_order.append(event.kind)
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final = stt_session.finish()
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event_order.append(final.kind)
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for llm_event in llm.stream([Message("user", final.text, 1.0)], cancellation=graph.root):
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event_order.append(f"llm_{llm_event.kind}")
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if llm_event.text_delta:
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for sentence in segmenter.accept_delta(llm_event.text_delta):
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event_order.append("sentence")
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frames = tts_session.accept_text(sentence)
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event_order.append("tts")
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playback.enqueue(sentence, frames)
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result = playback.play_next(render_reference=render, cancellation=graph.root)
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event_order.append("playback")
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self.assertEqual(
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event_order,
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["partial", "stable_partial", "final", "llm_delta", "sentence", "tts", "llm_finish", "playback"],
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)
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self.assertFalse(result.interrupted)
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self.assertEqual(playback.spoken.text, "你好。")
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self.assertEqual(render.frame_count, 1)
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def test_full_duplex_runtime_streaming_response_writes_render_reference_and_spoken_text(self) -> None:
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runtime = FullDuplexAgentRuntime(
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config=AppConfig(audio_apm_provider="fake", audio_apm_required=False),
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llm_provider=FakeStreamingLlmProvider(
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[LlmStreamEvent("delta", "第一句。第二句。"), LlmStreamEvent("finish", finish_reason="stop")]
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),
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tts_provider=FakeStreamingTtsProvider(),
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)
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spoken = runtime.run_streaming_response_fixture([Message("user", "你好", 1.0)])
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self.assertEqual(spoken, "第一句。第二句。")
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self.assertIsNotNone(runtime.audio_hub)
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self.assertEqual(runtime.audio_hub.render_reference.frame_count, 2)
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self.assertEqual(runtime.playback_queue.pending_items, 0)
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def test_full_duplex_runtime_injects_memory_context_before_llm(self) -> None:
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memory = FakeMemoryManager()
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memory.save(MemoryRecordInput("preference", "用户喜欢 Python"))
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llm = FakeStreamingLlmProvider([LlmStreamEvent("delta", "记住了。"), LlmStreamEvent("finish", finish_reason="stop")])
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runtime = FullDuplexAgentRuntime(
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config=AppConfig(audio_apm_provider="fake", audio_apm_required=False, memory_enabled=True),
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memory_manager=memory,
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llm_provider=llm,
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tts_provider=FakeStreamingTtsProvider(),
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)
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spoken = runtime.run_conversation_response_fixture("Python 项目怎么做？")
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self.assertEqual(spoken, "记住了。")
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self.assertIn("长期记忆", llm.requests[0][1].content)
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self.assertIn("用户喜欢 Python", llm.requests[0][1].content)
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self.assertEqual([message.role for message in runtime.context.messages()], ["user", "assistant"])
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def test_full_duplex_runtime_memory_health_detects_manifest_mismatch(self) -> None:
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with tempfile.TemporaryDirectory() as tmp:
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memory = SQLiteMemoryManager(Path(tmp) / "memory.sqlite3")
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saved = memory.save(MemoryRecordInput("fact", "Owner 正在做全双工语音助手"))
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broken = FaissIndexManifest(
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embedding_model="fake",
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record_ids=(saved.id, "missing-id"),
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checksums={saved.id: "wrong"},
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)
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runtime = FullDuplexAgentRuntime(
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config=AppConfig(audio_apm_provider="fake", audio_apm_required=False, memory_enabled=True),
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memory_manager=memory,
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memory_manifest=broken,
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)
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health = runtime.check_memory_health()
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self.assertFalse(health.ok)
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self.assertTrue(any("missing-id" in error for error in health.errors))
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self.assertTrue(any("checksum mismatch" in error for error in health.errors))
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def test_full_duplex_runtime_routes_memory_search_tool_call(self) -> None:
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memory = FakeMemoryManager()
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memory.save(MemoryRecordInput("project", "Owner 项目正在做全双工 Agent"))
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runtime = FullDuplexAgentRuntime(
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config=AppConfig(
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audio_apm_provider="fake",
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audio_apm_required=False,
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memory_enabled=True,
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tool_router_enabled=True,
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),
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memory_manager=memory,
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llm_provider=FakeStreamingLlmProvider(
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[
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LlmStreamEvent(
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"tool_call",
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tool_call={
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"id": "tool-1",
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"name": "memory.search",
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"arguments": {"query": "Owner Agent", "top_k": 1},
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"turn_id": "turn-1",
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},
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),
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LlmStreamEvent("delta", "查到了。"),
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LlmStreamEvent("finish", finish_reason="stop"),
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]
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),
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tts_provider=FakeStreamingTtsProvider(),
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)
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spoken = runtime.run_conversation_response_fixture("查一下当前项目")
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self.assertEqual(spoken, "查到了。")
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self.assertEqual(runtime.tool_results[0].status, "success")
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self.assertIn("全双工 Agent", runtime.tool_results[0].output_text)
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self.assertIn("工具结果 memory.search", runtime.tool_result_messages[0].content)
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def test_full_duplex_runtime_high_risk_tool_call_requires_confirmation(self) -> None:
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runtime = FullDuplexAgentRuntime(
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config=AppConfig(
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audio_apm_provider="fake",
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audio_apm_required=False,
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memory_enabled=True,
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tool_router_enabled=True,
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),
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memory_manager=FakeMemoryManager(),
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llm_provider=FakeStreamingLlmProvider(
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[
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LlmStreamEvent(
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"tool_call",
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tool_call={
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"id": "tool-1",
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"name": "memory.search",
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"arguments": {"query": "账号"},
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"natural_language_intent": "上传账号资料",
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"turn_id": "turn-1",
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},
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),
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LlmStreamEvent("finish", finish_reason="stop"),
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]
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),
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tts_provider=FakeStreamingTtsProvider(),
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)
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runtime.run_conversation_response_fixture("上传账号资料")
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self.assertEqual(runtime.tool_results[0].status, "confirmation_required")
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self.assertEqual(runtime.tool_router.audit_log[0].action, "require_confirmation")
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def test_memory_restart_and_tool_search_integration(self) -> None:
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with tempfile.TemporaryDirectory() as tmp:
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db_path = Path(tmp) / "memory.sqlite3"
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SQLiteMemoryManager(db_path).save(MemoryRecordInput("preference", "用户喜欢 Python"))
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restarted = SQLiteMemoryManager(db_path)
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router = ToolRouter({"memory.search": MemorySearchTool()})
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request = ToolCallRequest("1", "memory.search", {"query": "Python"}, "turn")
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decision = router.route(request, ToolContext(memory=restarted))
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result = router.execute(request, decision, ToolContext(memory=restarted))
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self.assertEqual(result.status, "success")
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self.assertIn("用户喜欢 Python", result.output_text)
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def test_tool_router_security_blocks_high_risk_fake_integration(self) -> None:
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router = ToolRouter({"memory.search": MemorySearchTool()})
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request = ToolCallRequest(
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"1",
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"memory.search",
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{"query": "账号"},
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"turn",
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natural_language_intent="上传账号资料",
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)
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decision = router.route(request, ToolContext(memory=FakeMemoryManager()))
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self.assertEqual(decision.action, "require_confirmation")
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self.assertEqual(decision.risk_level, "high")
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def test_performance_metrics_are_sanitized(self) -> None:
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recorder = PerformanceMetricRecorder()
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recorder.record(
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"interrupt_latency",
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started_at_ms=100,
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finished_at_ms=250,
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payload={"api_key": "secret", "preview": "tp-" + "abcdefghijklmnop"},
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)
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self.assertEqual(recorder.summary()["interrupt_latency"], 150)
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self.assertEqual(recorder.metrics[0].payload["api_key"], "[redacted]")
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self.assertFalse(diagnostics_contain_sensitive_data(recorder.metrics[0].payload))
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def test_sanitize_diagnostics_removes_nested_sensitive_values(self) -> None:
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sanitized = sanitize_diagnostics(
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{
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"nested": {"authorization": "Bearer secret", "raw_audio": b"bytes"},
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"text": "normal",
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}
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)
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self.assertEqual(sanitized["nested"]["authorization"], "[redacted]")
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self.assertEqual(sanitized["nested"]["raw_audio"], "[redacted]")
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self.assertFalse(diagnostics_contain_sensitive_data(sanitized))
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if __name__ == "__main__":
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unittest.main()
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