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fix: llm generation in case of user idle

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Send for LLM generation in case of user idle rather than speaking a hardcoded sentence
This commit is contained in:
Abhishek Kumar 2026-01-03 16:22:38 +05:30
parent 56953bbd09
commit 04576ac357
11 changed files with 364 additions and 87 deletions
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47
api/tests/test_pipecat_engine_tool_calls.py
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@ -10,18 +10,9 @@ from unittest.mock import AsyncMock, patch
import pytest
from api.services.pipecat.pipeline_engine_callbacks_processor import (
PipelineEngineCallbacksProcessor,
)
from api.services.workflow.pipecat_engine import PipecatEngine
from api.services.workflow.workflow import WorkflowGraph
from api.tests.conftest import END_CALL_SYSTEM_PROMPT
from pipecat.frames.frames import (
BotStartedSpeakingFrame,
BotStoppedSpeakingFrame,
Frame,
TextFrame,
)
from api.tests.conftest import END_CALL_SYSTEM_PROMPT, MockTransportProcessor
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@ -30,33 +21,7 @@ from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorPa
from pipecat.processors.aggregators.llm_response_universal import (
LLMContextAggregatorPair,
)
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.tests import MockLLMService
class MockBotStoppedSpeakingOnLLMTextFrameProcessor(FrameProcessor):
"""
Mocking the transport, where transport sends BotStartedSpeakingFrame
and BotStoppedSpeakingFrame when it encounters a LLMTextFrame.
"""
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, TextFrame):
await self.push_frame(BotStartedSpeakingFrame())
await self.push_frame(
BotStartedSpeakingFrame(), direction=FrameDirection.UPSTREAM
)
await asyncio.sleep(0.1)
await self.push_frame(BotStoppedSpeakingFrame())
await self.push_frame(
BotStoppedSpeakingFrame(), direction=FrameDirection.UPSTREAM
)
await self.push_frame(frame, direction)
from pipecat.tests import MockLLMService, MockTTSService
async def run_pipeline_with_tool_calls(
@ -96,7 +61,10 @@ async def run_pipeline_with_tool_calls(
# Create MockLLMService with multi-step support
llm = MockLLMService(mock_steps=mock_steps, chunk_delay=0.001)
mock_transport_emulator = MockBotStoppedSpeakingOnLLMTextFrameProcessor()
# Create MockTTSService to generate TTS frames
tts = MockTTSService(mock_audio_duration_ms=10, frame_delay=0)
mock_transport_emulator = MockTransportProcessor(emit_bot_speaking=False)
# Create LLM context
context = LLMContext()
@ -117,10 +85,11 @@ async def run_pipeline_with_tool_calls(
workflow_run_id=1,
)
# Create the pipeline with the mock LLM
# Create the pipeline with the mock LLM and TTS
pipeline = Pipeline(
[
llm,
tts,
mock_transport_emulator,
assistant_context_aggregator,
]