mirror of
https://github.com/dograh-hq/dograh.git
synced 2026-07-19 11:41:04 +02:00
chore: refactor and add tests (#130)
* chore: add tests for end call * Update pipecat module * fix: allow interruptions from deepgram flux * Add VadUserTurnStrategy * chore: add test for voicemail detection
This commit is contained in:
Abhishek
GitHub
parent
2aedb839ff
commit
033fde8946
15 changed files with 2106 additions and 542 deletions
238
api/tests/test_voicemail_detector.py
Normal file
238
api/tests/test_voicemail_detector.py
Normal file
|
|
@ -0,0 +1,238 @@
|
|||
"""Tests for understanding voicemail detector behavior with user aggregator and LLM.
|
||||
|
||||
This module tests the interaction between the voicemail detector, user aggregator,
|
||||
and LLM in a pipeline. It demonstrates how the voicemail detector classifies
|
||||
incoming speech as CONVERSATION or VOICEMAIL and how the main LLM responds.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
|
||||
import pytest
|
||||
|
||||
from pipecat.extensions.voicemail.voicemail_detector import VoicemailDetector
|
||||
from pipecat.frames.frames import (
|
||||
EndTaskFrame,
|
||||
Frame,
|
||||
TranscriptionFrame,
|
||||
UserStartedSpeakingFrame,
|
||||
UserStoppedSpeakingFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.tests import MockLLMService
|
||||
from pipecat.turns.user_start import (
|
||||
TranscriptionUserTurnStartStrategy,
|
||||
VADUserTurnStartStrategy,
|
||||
)
|
||||
from pipecat.turns.user_stop import (
|
||||
ExternalUserTurnStopStrategy,
|
||||
)
|
||||
from pipecat.turns.user_turn_strategies import UserTurnStrategies
|
||||
from pipecat.utils.time import time_now_iso8601
|
||||
|
||||
|
||||
class FrameInjector(FrameProcessor):
|
||||
"""Simple processor that can inject frames into the pipeline."""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self._frames_to_inject: list[Frame] = []
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
async def inject_frame(
|
||||
self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM
|
||||
):
|
||||
"""Inject a frame into the pipeline."""
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
class FrameCounter:
|
||||
"""Helper to count specific frame types seen by a processor."""
|
||||
|
||||
def __init__(self):
|
||||
self.user_stopped_speaking_count = 0
|
||||
self.user_started_speaking_count = 0
|
||||
|
||||
def wrap_process_frame(self, original_process_frame):
|
||||
"""Wrap a process_frame method to count UserStoppedSpeakingFrame."""
|
||||
|
||||
async def wrapped(frame: Frame, direction: FrameDirection):
|
||||
if isinstance(frame, UserStoppedSpeakingFrame):
|
||||
self.user_stopped_speaking_count += 1
|
||||
elif isinstance(frame, UserStartedSpeakingFrame):
|
||||
self.user_started_speaking_count += 1
|
||||
return await original_process_frame(frame, direction)
|
||||
|
||||
return wrapped
|
||||
|
||||
|
||||
class TestVoicemailDetectorWithUserAggregator:
|
||||
"""Test scenarios with voicemail detector and user aggregator."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_voicemail_detector_conversation_flow(self):
|
||||
"""Test: Voicemail detector classifies as CONVERSATION and main LLM responds.
|
||||
|
||||
This test bench shows the flow:
|
||||
1. User starts speaking, sends transcription, stops speaking
|
||||
2. Voicemail detector's internal LLM classifies as "CONVERSATION"
|
||||
3. Main LLM generates response text
|
||||
4. Second user turn with transcription
|
||||
5. Main LLM generates end_call function to end pipeline
|
||||
|
||||
Pipeline structure mirrors run_pipeline.py:
|
||||
injector -> voicemail_detector.detector() -> user_aggregator -> main_llm
|
||||
-> voicemail_detector.gate() -> assistant_aggregator
|
||||
"""
|
||||
context = LLMContext()
|
||||
|
||||
# Create user turn strategies
|
||||
user_turn_strategies = UserTurnStrategies(
|
||||
start=[
|
||||
VADUserTurnStartStrategy(),
|
||||
TranscriptionUserTurnStartStrategy(),
|
||||
],
|
||||
stop=[ExternalUserTurnStopStrategy()],
|
||||
)
|
||||
|
||||
user_params = LLMUserAggregatorParams(
|
||||
user_turn_strategies=user_turn_strategies,
|
||||
)
|
||||
|
||||
assistant_params = LLMAssistantAggregatorParams(expect_stripped_words=True)
|
||||
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context, assistant_params=assistant_params, user_params=user_params
|
||||
)
|
||||
user_context_aggregator = context_aggregator.user()
|
||||
assistant_context_aggregator = context_aggregator.assistant()
|
||||
|
||||
# Create mock LLM for main conversation
|
||||
# Step 0: First response after CONVERSATION classification
|
||||
# Step 1: Response to second user turn
|
||||
# Step 2: end_call function call to end pipeline
|
||||
main_llm_steps = [
|
||||
MockLLMService.create_text_chunks(text="Hello! I'm here to help you today.")
|
||||
]
|
||||
main_llm = MockLLMService(mock_steps=main_llm_steps, chunk_delay=0.001)
|
||||
|
||||
# Create mock LLM for voicemail classification
|
||||
# First response: "CONVERSATION" to close the voicemail gate
|
||||
voicemail_classification_steps = [
|
||||
MockLLMService.create_text_chunks(text="CONVERSATION"),
|
||||
]
|
||||
voicemail_llm = MockLLMService(
|
||||
mock_steps=voicemail_classification_steps, chunk_delay=0.001
|
||||
)
|
||||
|
||||
# Create voicemail detector with the classification LLM
|
||||
voicemail_detector = VoicemailDetector(
|
||||
llm=voicemail_llm,
|
||||
voicemail_response_delay=0,
|
||||
)
|
||||
|
||||
# Set up frame counter to track UserStoppedSpeakingFrame in voicemail detector's user aggregator
|
||||
voicemail_user_aggregator = voicemail_detector._context_aggregator.user()
|
||||
frame_counter = FrameCounter()
|
||||
original_process_frame = voicemail_user_aggregator.process_frame
|
||||
voicemail_user_aggregator.process_frame = frame_counter.wrap_process_frame(
|
||||
original_process_frame
|
||||
)
|
||||
|
||||
# Build pipeline similar to run_pipeline.py structure
|
||||
injector = FrameInjector()
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
injector,
|
||||
voicemail_detector.detector(), # Classification parallel pipeline
|
||||
user_context_aggregator,
|
||||
main_llm,
|
||||
assistant_context_aggregator,
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(pipeline, params=PipelineParams(), enable_rtvi=False)
|
||||
runner = PipelineRunner()
|
||||
|
||||
async def run_pipeline():
|
||||
await runner.run(task)
|
||||
|
||||
async def inject_frames():
|
||||
await asyncio.sleep(0.05)
|
||||
|
||||
# === First user turn ===
|
||||
# This triggers voicemail classification AND main LLM response
|
||||
await injector.inject_frame(UserStartedSpeakingFrame())
|
||||
await asyncio.sleep(0)
|
||||
await injector.inject_frame(
|
||||
TranscriptionFrame("First User Speech", "user-123", time_now_iso8601())
|
||||
)
|
||||
await asyncio.sleep(0.05)
|
||||
await injector.inject_frame(UserStoppedSpeakingFrame())
|
||||
|
||||
# Wait for voicemail classification and main LLM response
|
||||
await asyncio.sleep(0.2)
|
||||
|
||||
# === Second user turn ===
|
||||
await injector.inject_frame(UserStartedSpeakingFrame())
|
||||
|
||||
await asyncio.sleep(0)
|
||||
await injector.inject_frame(
|
||||
TranscriptionFrame(
|
||||
"Second User Speech",
|
||||
"user-123",
|
||||
time_now_iso8601(),
|
||||
)
|
||||
)
|
||||
|
||||
await asyncio.sleep(0.05)
|
||||
await injector.inject_frame(UserStoppedSpeakingFrame())
|
||||
|
||||
await asyncio.sleep(0.05)
|
||||
await injector.inject_frame(
|
||||
EndTaskFrame(), direction=FrameDirection.UPSTREAM
|
||||
)
|
||||
|
||||
await asyncio.gather(run_pipeline(), inject_frames())
|
||||
|
||||
# Assert voicemail LLM was called once for classification
|
||||
assert voicemail_llm.get_current_step() == 1
|
||||
|
||||
# Assert main LLM was called twice (once per user turn)
|
||||
assert main_llm.get_current_step() == 2
|
||||
|
||||
# Assert voicemail detector's user aggregator saw UserStoppedSpeakingFrame only once
|
||||
# (because the classifier gate closes after CONVERSATION classification,
|
||||
# blocking subsequent frames from reaching the voicemail branch)
|
||||
assert frame_counter.user_stopped_speaking_count == 1, (
|
||||
f"Expected voicemail detector's user aggregator to see UserStoppedSpeakingFrame once, "
|
||||
f"but saw it {frame_counter.user_stopped_speaking_count} times"
|
||||
)
|
||||
|
||||
# We should see no more than 2 user started speaking frame. One from downstream FrameInjector
|
||||
# and one from upstream main pipeline's LLMUserAggregator
|
||||
assert frame_counter.user_started_speaking_count <= 2, (
|
||||
f"Expected voicemail detector's user aggregator to see UserStartedSpeakingFrame at most twice, "
|
||||
f"but saw it {frame_counter.user_started_speaking_count} times"
|
||||
)
|
||||
|
||||
# Assert the classifier gate is closed after classification
|
||||
assert voicemail_detector._classifier_gate._gate_opened is False, (
|
||||
"Expected classifier gate to be closed after CONVERSATION classification"
|
||||
)
|
||||
|
||||
# Assert the classifier gate is closed after classification
|
||||
assert voicemail_detector._classifier_upstream_gate._gate_open is False, (
|
||||
"Expected classifier upstream gate to be closed after CONVERSATION classification"
|
||||
)
|
||||
Loading…
Add table
Add a link
Reference in a new issue