292 lines
11 KiB
Python
292 lines
11 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 os
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import sys
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from typing import Optional
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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 EndTaskFrame, LLMMessagesUpdateFrame
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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
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from pipecat.services.ai_services import LLMService
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.google import GoogleLLMContext, GoogleLLMService
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from pipecat.transports.services.daily import DailyDialinSettings, DailyParams, DailyTransport
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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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async def terminate_call(
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function_name, tool_call_id, args, llm: LLMService, context, result_callback
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):
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"""Function the bot can call to terminate the call upon completion of a voicemail message."""
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await llm.queue_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
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async def respond_with_apple(
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function_name, tool_call_id, args, llm: LLMService, context: GoogleLLMContext, result_callback
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):
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messages = [
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{
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"role": "system",
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"content": "Always respond with Apple",
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}
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]
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print("respond_with_apple")
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# context.system_message = "Always respond with Apple"
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print(f"context before: {context.tools}")
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await llm.push_frame(LLMMessagesUpdateFrame(messages))
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print(f"context after: {context.tools}")
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async def respond_with_banana(
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function_name, tool_call_id, args, llm: LLMService, context: GoogleLLMContext, result_callback
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):
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messages = [
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{
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"role": "system",
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"content": "Always respond with Banana",
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}
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]
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print("respond_with_banana")
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# context.system_message = "Always respond with Banana"
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print(f"context before: {context.tools}")
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await llm.push_frame(LLMMessagesUpdateFrame(messages))
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print(f"context after: {context.tools}")
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async def respond_with_orange(
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function_name, tool_call_id, args, llm: LLMService, context: GoogleLLMContext, result_callback
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):
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messages = [
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{
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"role": "system",
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"content": "Always respond with Orange",
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}
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]
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print("respond_with_orange")
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# context.system_message = "Always respond with Orange"
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print(f"context before: {context.tools}")
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await llm.push_frame(LLMMessagesUpdateFrame(messages))
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print(f"context after: {context.tools}")
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async def main(
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room_url: str,
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token: str,
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callId: str,
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callDomain: str,
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detect_voicemail: bool,
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dialout_number: Optional[str],
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):
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# dialin_settings are only needed if Daily's SIP URI is used
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# If you are handling this via Twilio, Telnyx, set this to None
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# and handle call-forwarding when on_dialin_ready fires.
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dialin_settings = DailyDialinSettings(call_id=callId, call_domain=callDomain)
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transport = DailyTransport(
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room_url,
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token,
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"Chatbot",
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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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dialin_settings=dialin_settings,
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audio_in_enabled=True,
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audio_out_enabled=True,
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camera_out_enabled=False,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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transcription_enabled=True,
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),
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)
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tts = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY", ""),
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voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
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)
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tools = [
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{
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"function_declarations": [
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{
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"name": "respond_with_banana",
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"description": "Call this function when the user asks about bananas.",
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},
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{
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"name": "respond_with_orange",
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"description": "Call this function when the user asks about oranges.",
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},
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{
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"name": "respond_with_apple",
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"description": "Call this function when the user asks about apples.",
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},
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]
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}
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]
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system_instruction2 = """You are Chatbot, a friendly, helpful robot.
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IMPORTANT: You MUST use the terminate_call function to end the call in these situations:
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1. After leaving a voicemail message
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2. When the conversation with a human is finished
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VOICEMAIL DETECTION:
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- Listen carefully for these exact phrases at the start of the call:
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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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IF VOICEMAIL DETECTED:
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1. Wait for any beep sound if mentioned
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2. Say EXACTLY: "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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3. IMMEDIATELY call the terminate_call function after your message
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4. Do not say anything else
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IF HUMAN DETECTED:
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1. Say: "Oh, hello! I'm a friendly chatbot. Is there anything I can help you with?"
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2. Keep responses short and helpful
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3. When conversation ends, say: "Okay, thank you! Have a great day!"
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4. IMMEDIATELY call the terminate_call function
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NEVER say these phrases yourself:
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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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"""
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system_instruction3 = """
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You are Chatbot. Your MAIN GOAL is to call the terminate_call function at the end.
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After each response, YOU MUST call the terminate_call function. This is REQUIRED.
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If someone says "Please leave a message after the beep":
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Say: "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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If someone says anything else:
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Say: "Hello, I'm Chatbot. Nice to meet you."
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IMPORTANT: YOU MUST CALL the terminate_call function after you respond.
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terminate_call is the ONLY way to end the call properly.
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"""
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system_instruction1 = """You are Chatbot. Follow these exact steps in order:
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1. Say "Hi, I'm Chatbot! Here's a joke: Why don't scientists trust atoms? Because they make up everything!"
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2. IMMEDIATELY after telling the joke, call the function terminate_call"""
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system_instruction = """Always respond with the word Apple"""
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llm = GoogleLLMService(
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model="models/gemini-2.0-flash-lite-preview-02-05",
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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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# llm.register_function("terminate_call", terminate_call)
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llm.register_function("respond_with_apple", respond_with_apple)
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llm.register_function("respond_with_banana", respond_with_banana)
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llm.register_function("respond_with_orange", respond_with_orange)
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context = GoogleLLMContext()
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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context_aggregator.user(), # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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context_aggregator.assistant(), # Assistant spoken responses
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]
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)
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task = PipelineTask(
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pipeline,
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PipelineParams(allow_interruptions=True),
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)
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if dialout_number:
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logger.debug("dialout number detected; doing dialout")
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# Configure some handlers for dialing out
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@transport.event_handler("on_joined")
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async def on_joined(transport, data):
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logger.debug(f"Joined; starting dialout to: {dialout_number}")
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await transport.start_dialout({"phoneNumber": dialout_number})
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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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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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await transport.capture_participant_transcription(participant["id"])
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# unlike the dialin case, for the dialout case, the caller will speak first. Presumably
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# they will answer the phone and say "Hello?" Since we've captured their transcript,
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# That will put a frame into the pipeline and prompt an LLM completion, which is how the
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# bot will then greet the user.
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elif detect_voicemail:
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logger.debug("Detect voicemail example. You can test this in example in Daily Prebuilt")
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# For the voicemail detection case, we do not want the bot to answer the phone. We want it to wait for the voicemail
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# machine to say something like 'Leave a message after the beep', or for the user to say 'Hello?'.
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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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await transport.capture_participant_transcription(participant["id"])
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else:
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logger.debug("no dialout number; assuming dialin")
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# Different handlers for dialin
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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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await transport.capture_participant_transcription(participant["id"])
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# For the dialin case, we want the bot to answer the phone and greet the user. We
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# can prompt the bot to speak by putting the context into the pipeline.
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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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 task.cancel()
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Pipecat Simple ChatBot")
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parser.add_argument("-u", type=str, help="Room URL")
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parser.add_argument("-t", type=str, help="Token")
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parser.add_argument("-i", type=str, help="Call ID")
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parser.add_argument("-d", type=str, help="Call Domain")
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parser.add_argument("-v", action="store_true", help="Detect voicemail")
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parser.add_argument("-o", type=str, help="Dialout number", default=None)
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config = parser.parse_args()
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asyncio.run(main(config.u, config.t, config.i, config.d, config.v, config.o))
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