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

api/tests/test_pipecat_engine_variable_extraction.py

@@ -17,23 +17,11 @@ from unittest.mock import AsyncMock, patch
 
 import pytest
 
-from api.services.workflow.dto import (
-    EdgeDataDTO,
-    ExtractionVariableDTO,
-    NodeDataDTO,
-    NodeType,
-    Position,
-    ReactFlowDTO,
-    RFEdgeDTO,
-    RFNodeDTO,
-    VariableType,
-)
 from api.services.workflow.pipecat_engine import PipecatEngine
 from api.services.workflow.pipecat_engine_variable_extractor import (
     VariableExtractionManager,
 )
 from api.services.workflow.workflow import WorkflowGraph
-from api.tests.conftest import MockTransportProcessor
 from pipecat.frames.frames import LLMContextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -44,96 +32,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
 )
 from pipecat.tests import MockLLMService, MockTTSService
-
-# Define prompts for test nodes
-START_NODE_PROMPT = "Start Node System Prompt"
-AGENT_NODE_PROMPT = "Agent Node System Prompt"
-END_NODE_PROMPT = "End Node System Prompt"
-
-
-@pytest.fixture
-def three_node_workflow_with_extraction_on_start() -> WorkflowGraph:
-    """Create a three-node workflow where only the start node has extraction enabled.
-
-    The workflow has:
-    - Start node with extraction_enabled=True and extraction_variables set
-    - Agent node with extraction_enabled=False (default)
-    - End node with extraction_enabled=False (default)
-
-    This is used to test that variable extraction is triggered for the correct node
-    during transitions.
-    """
-    dto = ReactFlowDTO(
-        nodes=[
-            RFNodeDTO(
-                id="start",
-                type=NodeType.startNode,
-                position=Position(x=0, y=0),
-                data=NodeDataDTO(
-                    name="Start Call",
-                    prompt=START_NODE_PROMPT,
-                    is_start=True,
-                    allow_interrupt=False,
-                    add_global_prompt=False,
-                    extraction_enabled=True,
-                    extraction_prompt="Extract the user's name from the conversation.",
-                    extraction_variables=[
-                        ExtractionVariableDTO(
-                            name="user_name",
-                            type=VariableType.string,
-                            prompt="The name the user provided",
-                        ),
-                    ],
-                ),
-            ),
-            RFNodeDTO(
-                id="agent",
-                type=NodeType.agentNode,
-                position=Position(x=0, y=200),
-                data=NodeDataDTO(
-                    name="Collect Info",
-                    prompt=AGENT_NODE_PROMPT,
-                    allow_interrupt=False,
-                    add_global_prompt=False,
-                    extraction_enabled=False,  # Explicitly disabled
-                ),
-            ),
-            RFNodeDTO(
-                id="end",
-                type=NodeType.endNode,
-                position=Position(x=0, y=400),
-                data=NodeDataDTO(
-                    name="End Call",
-                    prompt=END_NODE_PROMPT,
-                    is_end=True,
-                    allow_interrupt=False,
-                    add_global_prompt=False,
-                    extraction_enabled=False,  # Explicitly disabled
-                ),
-            ),
-        ],
-        edges=[
-            RFEdgeDTO(
-                id="start-agent",
-                source="start",
-                target="agent",
-                data=EdgeDataDTO(
-                    label="Collect Info",
-                    condition="When user has been greeted, proceed to collect information",
-                ),
-            ),
-            RFEdgeDTO(
-                id="agent-end",
-                source="agent",
-                target="end",
-                data=EdgeDataDTO(
-                    label="End Call",
-                    condition="When information collection is complete, end the call",
-                ),
-            ),
-        ],
-    )
-    return WorkflowGraph(dto)
+from pipecat.tests.mock_transport import MockTransport
 
 
 class TestVariableExtractionDuringTransitions:
@@ -141,7 +40,7 @@ class TestVariableExtractionDuringTransitions:
 
     @pytest.mark.asyncio
     async def test_extraction_called_for_source_node_not_target_node(
-        self, three_node_workflow_with_extraction_on_start: WorkflowGraph
+        self, three_node_workflow_extraction_start_only: WorkflowGraph
     ):
         """Test that when transitioning from START to AGENT, extraction is called for START node.
 
@@ -183,7 +82,8 @@ class TestVariableExtractionDuringTransitions:
         # Create MockTTSService
         tts = MockTTSService(mock_audio_duration_ms=10, frame_delay=0)
 
-        mock_transport_emulator = MockTransportProcessor(emit_bot_speaking=False)
+        # Create MockTransport for simulating transport behavior
+        mock_transport = MockTransport(emit_bot_speaking=False)
 
         # Create LLM context
         context = LLMContext()
@@ -195,7 +95,7 @@ class TestVariableExtractionDuringTransitions:
         )
         assistant_context_aggregator = context_aggregator.assistant()
 
-        workflow = three_node_workflow_with_extraction_on_start
+        workflow = three_node_workflow_extraction_start_only
 
         # Create PipecatEngine with the workflow
         engine = PipecatEngine(
@@ -209,7 +109,7 @@ class TestVariableExtractionDuringTransitions:
         # Patch _perform_variable_extraction_if_needed to track calls
         original_perform_extraction = engine._perform_variable_extraction_if_needed
 
-        async def tracked_perform_extraction(node):
+        async def tracked_perform_extraction(node, run_in_background=True):
             extraction_calls.append(
                 {
                     "node_id": node.id if node else None,
@@ -228,7 +128,7 @@ class TestVariableExtractionDuringTransitions:
             [
                 llm,
                 tts,
-                mock_transport_emulator,
+                mock_transport.output(),
                 assistant_context_aggregator,
             ]
         )
@@ -236,7 +136,7 @@ class TestVariableExtractionDuringTransitions:
         # Create pipeline task
         task = PipelineTask(
             pipeline,
-            params=PipelineParams(allow_interruptions=False),
+            params=PipelineParams(),
             enable_rtvi=False,
         )