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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
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Abhishek
GitHub
parent
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commit
033fde8946
15 changed files with 2106 additions and 542 deletions
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@ -6,27 +6,20 @@ import pytest
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from api.services.workflow.dto import (
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EdgeDataDTO,
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ExtractionVariableDTO,
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NodeDataDTO,
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NodeType,
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Position,
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ReactFlowDTO,
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RFEdgeDTO,
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RFNodeDTO,
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VariableType,
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)
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from api.services.workflow.workflow import WorkflowGraph
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from pipecat.frames.frames import (
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BotSpeakingFrame,
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BotStartedSpeakingFrame,
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BotStoppedSpeakingFrame,
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Frame,
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TTSAudioRawFrame,
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TTSStartedFrame,
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TTSStoppedFrame,
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)
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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START_CALL_SYSTEM_PROMPT = "start_call_system_prompt"
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END_CALL_SYSTEM_PROMPT = "end_call_system_prompt"
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START_CALL_SYSTEM_PROMPT = "Start Call System Prompt"
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AGENT_SYSTEM_PROMPT = "Agent Node System Prompt"
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END_CALL_SYSTEM_PROMPT = "End Call System Prompt"
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# Default workflow definition for mocking database WorkflowModel
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DEFAULT_WORKFLOW_DEFINITION = {
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@ -110,57 +103,6 @@ class MockUserConfig:
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embeddings: Optional[Any] = None
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class MockTransportProcessor(FrameProcessor):
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"""
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Mocks the transport behavior by emitting Bot speaking frames
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when it encounters TTS frames.
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This simulates what a real transport would do when the bot is speaking:
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- TTSStartedFrame -> BotStartedSpeakingFrame
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- TTSAudioRawFrame -> BotSpeakingFrame
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- TTSStoppedFrame -> BotStoppedSpeakingFrame
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Args:
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emit_bot_speaking: If True, also emits BotSpeakingFrame on TTSAudioRawFrame
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which is needed for user idle tracking to start conversation tracking. Default True.
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"""
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def __init__(
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self,
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*,
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emit_bot_speaking: bool = True,
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**kwargs,
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):
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super().__init__(**kwargs)
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self._emit_bot_speaking = emit_bot_speaking
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if isinstance(frame, TTSStartedFrame):
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# Emit BotStartedSpeakingFrame to indicate bot started speaking
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await self.push_frame(BotStartedSpeakingFrame())
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await self.push_frame(
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BotStartedSpeakingFrame(), direction=FrameDirection.UPSTREAM
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)
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elif isinstance(frame, TTSAudioRawFrame):
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# Emit BotSpeakingFrame - this is what triggers user idle tracking
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# to start conversation tracking
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if self._emit_bot_speaking:
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await self.push_frame(BotSpeakingFrame())
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await self.push_frame(
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BotSpeakingFrame(), direction=FrameDirection.UPSTREAM
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)
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elif isinstance(frame, TTSStoppedFrame):
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# Emit BotStoppedSpeakingFrame to indicate bot stopped speaking
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await self.push_frame(BotStoppedSpeakingFrame())
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await self.push_frame(
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BotStoppedSpeakingFrame(), direction=FrameDirection.UPSTREAM
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)
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await self.push_frame(frame, direction)
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@dataclass
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class MockToolModel:
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"""Mock tool model for testing."""
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@ -299,14 +241,14 @@ def simple_workflow() -> WorkflowGraph:
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"""Create a simple two-node workflow for testing.
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The workflow has:
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- Start node with a prompt
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- Start node with extraction enabled (extracts user_intent)
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- End node with a prompt
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- One edge connecting them with label "End Call"
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"""
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dto = ReactFlowDTO(
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nodes=[
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RFNodeDTO(
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id="1",
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id="start",
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type=NodeType.startNode,
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position=Position(x=0, y=0),
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data=NodeDataDTO(
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@ -315,10 +257,19 @@ def simple_workflow() -> WorkflowGraph:
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is_start=True,
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allow_interrupt=False,
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add_global_prompt=False,
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extraction_enabled=True,
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extraction_prompt="Extract user information from the conversation.",
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extraction_variables=[
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ExtractionVariableDTO(
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name="user_intent",
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type=VariableType.string,
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prompt="The user's intent or reason for calling",
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),
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],
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),
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),
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RFNodeDTO(
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id="2",
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id="end",
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type=NodeType.endNode,
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position=Position(x=0, y=200),
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data=NodeDataDTO(
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@ -327,14 +278,15 @@ def simple_workflow() -> WorkflowGraph:
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is_end=True,
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allow_interrupt=False,
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add_global_prompt=False,
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extraction_enabled=False,
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),
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),
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],
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edges=[
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RFEdgeDTO(
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id="1-2",
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source="1",
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target="2",
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id="start-end",
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source="start",
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target="end",
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data=EdgeDataDTO(
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label="End Call",
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condition="When the user says to end the call, end the call",
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@ -350,37 +302,59 @@ def three_node_workflow() -> WorkflowGraph:
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"""Create a three-node workflow for testing with an intermediate agent node.
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The workflow has:
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- Start node
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- Agent node (for collecting information)
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- End node
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- Start node with extraction enabled (extracts greeting_type)
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- Agent node with extraction enabled (extracts user_name)
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- End node (no extraction)
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Edges:
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- Start -> Agent (label: "Collect Info")
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- Agent -> End (label: "End Call")
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"""
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dto = ReactFlowDTO(
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nodes=[
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RFNodeDTO(
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id="1",
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id="start",
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type=NodeType.startNode,
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position=Position(x=0, y=0),
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data=NodeDataDTO(
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name="Start Call",
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prompt=START_CALL_SYSTEM_PROMPT,
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is_start=True,
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allow_interrupt=True,
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allow_interrupt=False,
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add_global_prompt=False,
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extraction_enabled=True,
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extraction_prompt="Extract greeting information from the conversation.",
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extraction_variables=[
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ExtractionVariableDTO(
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name="greeting_type",
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type=VariableType.string,
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prompt="The type of greeting used",
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),
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],
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),
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),
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RFNodeDTO(
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id="2",
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id="agent",
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type=NodeType.agentNode,
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position=Position(x=0, y=200),
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data=NodeDataDTO(
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name="Collect Info",
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prompt="Help the user with their request. Ask clarifying questions if needed.",
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allow_interrupt=True,
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prompt=AGENT_SYSTEM_PROMPT,
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allow_interrupt=False,
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add_global_prompt=False,
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extraction_enabled=True,
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extraction_prompt="Extract user details from the conversation.",
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extraction_variables=[
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ExtractionVariableDTO(
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name="user_name",
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type=VariableType.string,
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prompt="The user's name",
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),
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],
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),
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),
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RFNodeDTO(
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id="3",
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id="end",
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type=NodeType.endNode,
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position=Position(x=0, y=400),
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data=NodeDataDTO(
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@ -389,26 +363,187 @@ def three_node_workflow() -> WorkflowGraph:
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is_end=True,
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allow_interrupt=False,
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add_global_prompt=False,
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extraction_enabled=False,
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),
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),
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],
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edges=[
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RFEdgeDTO(
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id="1-2",
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source="1",
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target="2",
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id="start-agent",
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source="start",
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target="agent",
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data=EdgeDataDTO(
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label="Collect Info",
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condition="When the user wants help, collect their information",
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condition="When user has been greeted, proceed to collect information",
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),
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),
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RFEdgeDTO(
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id="2-3",
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source="2",
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target="3",
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id="agent-end",
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source="agent",
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target="end",
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data=EdgeDataDTO(
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label="End Call",
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condition="When the user is done or wants to end the call",
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condition="When information collection is complete, end the call",
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),
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),
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],
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)
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return WorkflowGraph(dto)
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@pytest.fixture
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def three_node_workflow_extraction_start_only() -> WorkflowGraph:
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"""Create a three-node workflow with extraction enabled ONLY on start node.
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This fixture is specifically for testing that variable extraction is triggered
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for the correct node during transitions. The agent node has extraction disabled
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to verify extraction happens for the SOURCE node, not the TARGET node.
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The workflow has:
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- Start node with extraction enabled (extracts user_name)
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- Agent node with extraction DISABLED
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- End node (no extraction)
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"""
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dto = ReactFlowDTO(
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nodes=[
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RFNodeDTO(
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id="start",
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type=NodeType.startNode,
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position=Position(x=0, y=0),
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data=NodeDataDTO(
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name="Start Call",
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prompt=START_CALL_SYSTEM_PROMPT,
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is_start=True,
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allow_interrupt=False,
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add_global_prompt=False,
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extraction_enabled=True,
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extraction_prompt="Extract the user's name from the conversation.",
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extraction_variables=[
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ExtractionVariableDTO(
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name="user_name",
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type=VariableType.string,
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prompt="The name the user provided",
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),
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],
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),
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),
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RFNodeDTO(
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id="agent",
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type=NodeType.agentNode,
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position=Position(x=0, y=200),
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data=NodeDataDTO(
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name="Collect Info",
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prompt=AGENT_SYSTEM_PROMPT,
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allow_interrupt=False,
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add_global_prompt=False,
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extraction_enabled=False, # Explicitly disabled for testing
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),
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),
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RFNodeDTO(
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id="end",
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type=NodeType.endNode,
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position=Position(x=0, y=400),
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data=NodeDataDTO(
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name="End Call",
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prompt=END_CALL_SYSTEM_PROMPT,
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is_end=True,
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allow_interrupt=False,
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add_global_prompt=False,
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extraction_enabled=False,
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),
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),
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],
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edges=[
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RFEdgeDTO(
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id="start-agent",
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source="start",
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target="agent",
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data=EdgeDataDTO(
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label="Collect Info",
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condition="When user has been greeted, proceed to collect information",
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),
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),
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RFEdgeDTO(
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id="agent-end",
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source="agent",
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target="end",
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data=EdgeDataDTO(
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label="End Call",
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condition="When information collection is complete, end the call",
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),
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),
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],
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)
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return WorkflowGraph(dto)
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@pytest.fixture
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def three_node_workflow_no_variable_extraction() -> WorkflowGraph:
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"""Create a three-node workflow without variable extraction
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The workflow has:
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- Start node with extraction DISABLED
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- Agent node with extraction DISABLED
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- End node (no extraction)
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"""
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dto = ReactFlowDTO(
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nodes=[
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RFNodeDTO(
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id="start",
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type=NodeType.startNode,
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position=Position(x=0, y=0),
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data=NodeDataDTO(
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name="Start Call",
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prompt=START_CALL_SYSTEM_PROMPT,
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is_start=True,
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allow_interrupt=False,
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add_global_prompt=False,
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extraction_enabled=False,
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),
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),
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RFNodeDTO(
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id="agent",
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type=NodeType.agentNode,
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position=Position(x=0, y=200),
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data=NodeDataDTO(
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name="Collect Info",
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prompt=AGENT_SYSTEM_PROMPT,
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allow_interrupt=False,
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add_global_prompt=False,
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extraction_enabled=False, # Explicitly disabled for testing
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),
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),
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RFNodeDTO(
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id="end",
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type=NodeType.endNode,
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position=Position(x=0, y=400),
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data=NodeDataDTO(
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name="End Call",
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prompt=END_CALL_SYSTEM_PROMPT,
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is_end=True,
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allow_interrupt=False,
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add_global_prompt=False,
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extraction_enabled=False,
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),
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),
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],
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edges=[
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RFEdgeDTO(
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id="start-agent",
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source="start",
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target="agent",
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data=EdgeDataDTO(
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label="Collect Info",
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condition="When user has been greeted, proceed to collect information",
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),
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),
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RFEdgeDTO(
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id="agent-end",
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source="agent",
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target="end",
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data=EdgeDataDTO(
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label="End Call",
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condition="When information collection is complete, end the call",
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),
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),
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],
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