450 lines
17 KiB
Python
450 lines
17 KiB
Python
#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import argparse
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import asyncio
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import functools
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import os
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import sys
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from call_connection_manager import CallConfigManager, SessionManager
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import (
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EndFrame,
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EndTaskFrame,
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InputAudioRawFrame,
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StopTaskFrame,
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TranscriptionFrame,
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UserStartedSpeakingFrame,
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UserStoppedSpeakingFrame,
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)
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.google.google import GoogleLLMContext
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from pipecat.services.google.llm import GoogleLLMService
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from pipecat.services.llm_service import FunctionCallParams
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from pipecat.transports.services.daily import (
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DailyParams,
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DailyTransport,
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)
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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daily_api_key = os.getenv("DAILY_API_KEY", "")
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daily_api_url = os.getenv("DAILY_API_URL", "https://api.daily.co/v1")
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# ------------ HELPER CLASSES ------------
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class UserAudioCollector(FrameProcessor):
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"""Collects audio frames in a buffer, then adds them to the LLM context when the user stops speaking."""
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def __init__(self, context, user_context_aggregator):
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super().__init__()
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self._context = context
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self._user_context_aggregator = user_context_aggregator
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self._audio_frames = []
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self._start_secs = 0.2 # this should match VAD start_secs (hardcoding for now)
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self._user_speaking = False
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async def process_frame(self, frame, direction):
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await super().process_frame(frame, direction)
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if isinstance(frame, TranscriptionFrame):
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# Skip transcription frames - we're handling audio directly
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return
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elif isinstance(frame, UserStartedSpeakingFrame):
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self._user_speaking = True
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elif isinstance(frame, UserStoppedSpeakingFrame):
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self._user_speaking = False
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self._context.add_audio_frames_message(audio_frames=self._audio_frames)
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await self._user_context_aggregator.push_frame(
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self._user_context_aggregator.get_context_frame()
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)
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elif isinstance(frame, InputAudioRawFrame):
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if self._user_speaking:
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# When speaking, collect frames
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self._audio_frames.append(frame)
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else:
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# Maintain a rolling buffer of recent audio (for start of speech)
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self._audio_frames.append(frame)
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frame_duration = len(frame.audio) / 16 * frame.num_channels / frame.sample_rate
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buffer_duration = frame_duration * len(self._audio_frames)
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while buffer_duration > self._start_secs:
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self._audio_frames.pop(0)
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buffer_duration -= frame_duration
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await self.push_frame(frame, direction)
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class FunctionHandlers:
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"""Handlers for the voicemail detection bot functions."""
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def __init__(self, session_manager):
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self.session_manager = session_manager
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self.prompt = None # Can be set externally
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async def voicemail_response(self, params: FunctionCallParams):
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"""Function the bot can call to leave a voicemail message."""
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message = """You are Chatbot leaving a voicemail message. Say EXACTLY this message and then terminate the call:
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'Hello, this is a message for Pipecat example user. This is Chatbot. Please call back on 123-456-7891. Thank you.'"""
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await params.result_callback(message)
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async def human_conversation(self, params: FunctionCallParams):
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"""Function called when bot detects it's talking to a human."""
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# Update state to indicate human was detected
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self.session_manager.call_flow_state.set_human_detected()
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await params.llm.push_frame(StopTaskFrame(), FrameDirection.UPSTREAM)
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# ------------ MAIN FUNCTION ------------
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async def main(
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room_url: str,
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token: str,
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body: dict,
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):
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# ------------ CONFIGURATION AND SETUP ------------
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# Create a configuration manager from the provided body
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call_config_manager = CallConfigManager.from_json_string(body) if body else CallConfigManager()
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# Get important configuration values
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dialout_settings = call_config_manager.get_dialout_settings()
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test_mode = call_config_manager.is_test_mode()
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# Get caller info (might be None for dialout scenarios)
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caller_info = call_config_manager.get_caller_info()
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logger.info(f"Caller info: {caller_info}")
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# Initialize the session manager
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session_manager = SessionManager()
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# ------------ TRANSPORT AND SERVICES SETUP ------------
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# Initialize transport
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transport = DailyTransport(
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room_url,
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token,
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"Voicemail Detection Bot",
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DailyParams(
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api_url=daily_api_url,
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api_key=daily_api_key,
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audio_in_enabled=True,
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audio_out_enabled=True,
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video_out_enabled=False,
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vad_analyzer=SileroVADAnalyzer(),
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),
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)
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# Initialize TTS
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY", ""),
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voice_id="b7d50908-b17c-442d-ad8d-810c63997ed9", # Use Helpful Woman voice by default
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)
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# Initialize speech-to-text service (for human conversation phase)
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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# ------------ FUNCTION DEFINITIONS ------------
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async def terminate_call(
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params: FunctionCallParams,
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session_manager=None,
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):
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"""Function the bot can call to terminate the call."""
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if session_manager:
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# Set call terminated flag in the session manager
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session_manager.call_flow_state.set_call_terminated()
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await params.llm.queue_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
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# ------------ VOICEMAIL DETECTION PHASE SETUP ------------
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# Define tools for both LLMs
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tools = [
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{
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"function_declarations": [
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{
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"name": "switch_to_voicemail_response",
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"description": "Call this function when you detect this is a voicemail system.",
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},
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{
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"name": "switch_to_human_conversation",
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"description": "Call this function when you detect this is a human.",
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},
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{
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"name": "terminate_call",
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"description": "Call this function to terminate the call.",
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},
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]
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}
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]
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# Get voicemail detection prompt
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voicemail_detection_prompt = call_config_manager.get_prompt("voicemail_detection_prompt")
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if voicemail_detection_prompt:
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system_instruction = voicemail_detection_prompt
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else:
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system_instruction = """You are Chatbot trying to determine if this is a voicemail system or a human.
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If you hear any of these phrases (or very similar ones):
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- "Please leave a message after the beep"
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- "No one is available to take your call"
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- "Record your message after the tone"
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- "You have reached voicemail for..."
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- "You have reached [phone number]"
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- "[phone number] is unavailable"
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- "The person you are trying to reach..."
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- "The number you have dialed..."
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- "Your call has been forwarded to an automated voice messaging system"
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Then call the function switch_to_voicemail_response.
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If it sounds like a human (saying hello, asking questions, etc.), call the function switch_to_human_conversation.
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DO NOT say anything until you've determined if this is a voicemail or human.
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If you are asked to terminate the call, **IMMEDIATELY** call the `terminate_call` function. **FAILURE TO CALL `terminate_call` IMMEDIATELY IS A MISTAKE.**"""
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# Initialize voicemail detection LLM
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voicemail_detection_llm = GoogleLLMService(
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model="models/gemini-2.0-flash-lite", # Lighter model for faster detection
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api_key=os.getenv("GOOGLE_API_KEY"),
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system_instruction=system_instruction,
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tools=tools,
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)
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# Initialize context and context aggregator
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voicemail_detection_context = GoogleLLMContext()
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voicemail_detection_context_aggregator = voicemail_detection_llm.create_context_aggregator(
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voicemail_detection_context
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)
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# Get custom voicemail prompt if available
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voicemail_prompt = call_config_manager.get_prompt("voicemail_prompt")
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# Set up function handlers
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handlers = FunctionHandlers(session_manager)
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handlers.prompt = voicemail_prompt # Set custom prompt if available
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# Register functions with the voicemail detection LLM
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voicemail_detection_llm.register_function(
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"switch_to_voicemail_response",
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handlers.voicemail_response,
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)
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voicemail_detection_llm.register_function(
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"switch_to_human_conversation", handlers.human_conversation
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)
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voicemail_detection_llm.register_function(
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"terminate_call", lambda params: terminate_call(params, session_manager)
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)
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# Set up audio collector for handling audio input
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voicemail_detection_audio_collector = UserAudioCollector(
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voicemail_detection_context, voicemail_detection_context_aggregator.user()
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)
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# Build voicemail detection pipeline
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voicemail_detection_pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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voicemail_detection_audio_collector, # Collect audio frames
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voicemail_detection_context_aggregator.user(), # User context
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voicemail_detection_llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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voicemail_detection_context_aggregator.assistant(), # Assistant context
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]
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)
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# Create pipeline task
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voicemail_detection_pipeline_task = PipelineTask(
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voicemail_detection_pipeline,
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params=PipelineParams(allow_interruptions=True),
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)
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# ------------ EVENT HANDLERS ------------
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@transport.event_handler("on_joined")
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async def on_joined(transport, data):
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# Start dialout if needed
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if not test_mode and dialout_settings:
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logger.debug("Dialout settings detected; starting dialout")
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await call_config_manager.start_dialout(transport, dialout_settings)
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@transport.event_handler("on_dialout_connected")
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async def on_dialout_connected(transport, data):
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logger.debug(f"Dial-out connected: {data}")
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@transport.event_handler("on_dialout_answered")
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async def on_dialout_answered(transport, data):
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logger.debug(f"Dial-out answered: {data}")
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# Start capturing transcription
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await transport.capture_participant_transcription(data["sessionId"])
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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logger.debug(f"First participant joined: {participant['id']}")
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if test_mode:
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await transport.capture_participant_transcription(participant["id"])
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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# Mark that a participant left early
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session_manager.call_flow_state.set_participant_left_early()
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await voicemail_detection_pipeline_task.queue_frame(EndFrame())
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# ------------ RUN VOICEMAIL DETECTION PIPELINE ------------
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if test_mode:
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logger.debug("Detect voicemail example. You can test this in Daily Prebuilt")
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runner = PipelineRunner()
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print("!!! starting voicemail detection pipeline")
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try:
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await runner.run(voicemail_detection_pipeline_task)
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except Exception as e:
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logger.error(f"Error in voicemail detection pipeline: {e}")
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import traceback
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logger.error(traceback.format_exc())
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print("!!! Done with voicemail detection pipeline")
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# Check if we should exit early
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if (
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session_manager.call_flow_state.participant_left_early
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or session_manager.call_flow_state.call_terminated
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):
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if session_manager.call_flow_state.participant_left_early:
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print("!!! Participant left early; terminating call")
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elif session_manager.call_flow_state.call_terminated:
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print("!!! Bot terminated call; not proceeding to human conversation")
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return
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# ------------ HUMAN CONVERSATION PHASE SETUP ------------
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# Get human conversation prompt
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human_conversation_prompt = call_config_manager.get_prompt("human_conversation_prompt")
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if human_conversation_prompt:
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human_conversation_system_instruction = human_conversation_prompt
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else:
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human_conversation_system_instruction = """You are Chatbot talking to a human. Be friendly and helpful.
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Start with: "Hello! I'm a friendly chatbot. How can I help you today?"
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Keep your responses brief and to the point. Listen to what the person says.
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When the person indicates they're done with the conversation by saying something like:
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- "Goodbye"
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- "That's all"
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- "I'm done"
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- "Thank you, that's all I needed"
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THEN say: "Thank you for chatting. Goodbye!" and call the terminate_call function."""
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# Initialize human conversation LLM
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human_conversation_llm = GoogleLLMService(
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model="models/gemini-2.0-flash-001", # Full model for better conversation
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api_key=os.getenv("GOOGLE_API_KEY"),
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system_instruction=human_conversation_system_instruction,
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tools=tools,
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)
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# Initialize context and context aggregator
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human_conversation_context = GoogleLLMContext()
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human_conversation_context_aggregator = human_conversation_llm.create_context_aggregator(
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human_conversation_context
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)
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# Register terminate function with the human conversation LLM
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human_conversation_llm.register_function(
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"terminate_call", functools.partial(terminate_call, session_manager=session_manager)
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)
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# Build human conversation pipeline
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human_conversation_pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt, # Speech-to-text
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human_conversation_context_aggregator.user(), # User context
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human_conversation_llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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human_conversation_context_aggregator.assistant(), # Assistant context
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]
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)
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# Create pipeline task
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human_conversation_pipeline_task = PipelineTask(
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human_conversation_pipeline,
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params=PipelineParams(allow_interruptions=True),
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)
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# Update participant left handler for human conversation phase
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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await voicemail_detection_pipeline_task.queue_frame(EndFrame())
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await human_conversation_pipeline_task.queue_frame(EndFrame())
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# ------------ RUN HUMAN CONVERSATION PIPELINE ------------
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print("!!! starting human conversation pipeline")
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# Initialize the context with system message
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human_conversation_context_aggregator.user().set_messages(
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[call_config_manager.create_system_message(human_conversation_system_instruction)]
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)
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# Queue the context frame to start the conversation
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await human_conversation_pipeline_task.queue_frames(
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[human_conversation_context_aggregator.user().get_context_frame()]
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)
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# Run the human conversation pipeline
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try:
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await runner.run(human_conversation_pipeline_task)
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except Exception as e:
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logger.error(f"Error in voicemail detection pipeline: {e}")
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import traceback
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logger.error(traceback.format_exc())
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print("!!! Done with human conversation pipeline")
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# ------------ SCRIPT ENTRY POINT ------------
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Pipecat Voicemail Detection Bot")
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parser.add_argument("-u", "--url", type=str, help="Room URL")
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parser.add_argument("-t", "--token", type=str, help="Room Token")
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parser.add_argument("-b", "--body", type=str, help="JSON configuration string")
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args = parser.parse_args()
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# Log the arguments for debugging
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logger.info(f"Room URL: {args.url}")
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logger.info(f"Token: {args.token}")
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logger.info(f"Body provided: {bool(args.body)}")
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asyncio.run(main(args.url, args.token, args.body))
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