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fix: changes to update pipecat version to 0.0.100 (#122)

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* feat: add stt evals

* add smart turn as provider

* chore: remove deprecations

* chore: format files

* fix: remove deprecated UserIdleProcessor

* fix: remove deprecated TranscriptProcessor

* chore: update pipecat submodule

* feat: add evals visualisation

* fix: trigger llm generation on client connected and pipeline started

* chore: update pipecat

* chore: update pipecat submodule

* Add tests

* fix: slow loading of workflow page

* chore: update pipecat submodule

* Show version after release

* Fixes #99

* fix: provider check for websocket connection

* Fixes #107

* Fix #96

* chore: fix documentation

* fix: cloudonix campaign call error

---------

Co-authored-by: Sabiha Khan <sabihak89@gmail.com>
This commit is contained in:
Abhishek 2026-01-23 18:53:59 +05:30 • committed by GitHub
parent a4367bd83b
commit 911c5ed416
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104 changed files with 16919 additions and 597 deletions
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51
api/tests/test_user_idle_handler.py
View file

@ -1,10 +1,10 @@
"""
Simulates a user idle condition and tests the behaviour
of the user idle processor.
of the user idle handler.
This module tests the behavior when the user becomes idle during a conversation,
ensuring the UserIdleProcessor properly triggers the callback and the engine
handles it correctly.
ensuring the user_idle_timeout in LLMUserAggregatorParams properly triggers
the on_user_turn_idle event and the engine handles it correctly.
"""
import asyncio
@ -23,8 +23,8 @@ 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.user_idle_processor import UserIdleProcessor
from pipecat.tests import MockLLMService, MockTTSService
@ -32,8 +32,8 @@ async def run_pipeline_with_user_idle(
workflow: WorkflowGraph,
user_idle_timeout: float = 0.2,
mock_steps: list | None = None,
) -> tuple[MockLLMService, LLMContext, UserIdleProcessor]:
"""Run a pipeline with UserIdleProcessor and simulate user idle condition.
) -> tuple[MockLLMService, LLMContext]:
"""Run a pipeline with user_idle_timeout and simulate user idle condition.
Args:
workflow: The workflow graph to use.
@ -42,7 +42,7 @@ async def run_pipeline_with_user_idle(
defaults to a simple greeting followed by text responses.
Returns:
Tuple of (MockLLMService, LLMContext, UserIdleProcessor) for assertions.
Tuple of (MockLLMService, LLMContext) for assertions.
"""
# Create mock responses - bot will speak first, then respond to idle prompts
# Step 1: Initial greeting
@ -64,10 +64,11 @@ async def run_pipeline_with_user_idle(
# Create LLM context
context = LLMContext()
# Create context aggregator with both user and assistant aggregators
# Create context aggregator with user_idle_timeout in user_params
assistant_params = LLMAssistantAggregatorParams(expect_stripped_words=True)
user_params = LLMUserAggregatorParams(user_idle_timeout=user_idle_timeout)
context_aggregator = LLMContextAggregatorPair(
context, assistant_params=assistant_params
context, assistant_params=assistant_params, user_params=user_params
)
user_context_aggregator = context_aggregator.user()
assistant_context_aggregator = context_aggregator.assistant()
@ -81,18 +82,20 @@ async def run_pipeline_with_user_idle(
workflow_run_id=1,
)
# Create UserIdleProcessor with engine's callback and a short timeout
user_idle_processor = UserIdleProcessor(
callback=engine.create_user_idle_callback(),
timeout=user_idle_timeout,
)
# Register user idle event handlers
user_idle_handler = engine.create_user_idle_handler()
# Build the pipeline:
# llm -> mock_transport -> user_idle_processor -> assistant_context_aggregator
# The user_context_aggregator would normally be at the start for user input
@user_context_aggregator.event_handler("on_user_turn_idle")
async def on_user_turn_idle(aggregator):
await user_idle_handler.handle_idle(aggregator)
@user_context_aggregator.event_handler("on_user_turn_started")
async def on_user_turn_started(aggregator, strategy):
user_idle_handler.reset()
# Build the pipeline
pipeline = Pipeline(
[
user_idle_processor,
user_context_aggregator,
llm,
tts,
@ -154,11 +157,11 @@ async def run_pipeline_with_user_idle(
return_exceptions=True,
)
return llm, context, user_idle_processor
return llm, context
class TestUserIdleHandler:
"""Test user idle handling through PipecatEngine and UserIdleProcessor."""
"""Test user idle handling through PipecatEngine and UserIdleHandler."""
@pytest.mark.asyncio
async def test_user_idle_triggers_callback(self, simple_workflow: WorkflowGraph):
@ -167,13 +170,13 @@ class TestUserIdleHandler:
This test verifies that when:
1. The bot starts speaking (triggers conversation tracking)
2. No user input is received for the timeout period
3. The UserIdleProcessor triggers the idle callback
3. The on_user_turn_idle event triggers the idle handler
The engine's user idle callback should:
The engine's user idle handler should:
- First retry: Send a message asking if user is still there
- Second retry: Send goodbye message and end the call
"""
llm, context, user_idle_processor = await run_pipeline_with_user_idle(
llm, context = await run_pipeline_with_user_idle(
workflow=simple_workflow,
user_idle_timeout=0.2, # Short timeout for faster test
)
@ -220,7 +223,7 @@ class TestUserIdleHandler:
MockLLMService.create_text_chunks("Response 3"),
]
llm, context, user_idle_processor = await run_pipeline_with_user_idle(
llm, context = await run_pipeline_with_user_idle(
workflow=three_node_workflow,
user_idle_timeout=0.2,
mock_steps=mock_steps,