240 lines
9.0 KiB
Python
240 lines
9.0 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 pprint import pprint
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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, LLMMessagesFrame, 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, FrameProcessor
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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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class ContextSwitcher:
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def __init__(self, llm, context_aggregator):
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self._llm = llm
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self._context_aggregator = context_aggregator
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async def switch_context(self, system_instruction):
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# Create messages with updated system instruction
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messages = [
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{
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"role": "system",
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"content": system_instruction,
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}
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]
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# Update context with new messages
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self._context_aggregator.set_messages(messages)
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context_frame = self._context_aggregator.get_context_frame()
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# Trigger LLM response by pushing a context frame
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pprint(vars(context_frame.context))
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await self._llm.push_frame(context_frame)
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class FunctionHandlers:
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def __init__(self, context_switcher):
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self.context_switcher = context_switcher
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async def respond_with_apple(
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self, function_name, tool_call_id, args, llm, context, result_callback
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):
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await self.context_switcher.switch_context(system_instruction="Always respond with Apple")
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await result_callback("Always respond with Apple")
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async def respond_with_banana(
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self, function_name, tool_call_id, args, llm, context, result_callback
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):
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await self.context_switcher.switch_context(system_instruction="Always respond with Banana")
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await result_callback("Always respond with banana")
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async def respond_with_oranges(
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self, function_name, tool_call_id, args, llm, context, result_callback
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):
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await self.context_switcher.switch_context(system_instruction="Always respond with Oranges")
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await result_callback("Always respond with oranges")
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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 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_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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context = GoogleLLMContext()
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context_aggregator = llm.create_context_aggregator(context)
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context_switcher = ContextSwitcher(llm, context_aggregator.user())
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handlers = FunctionHandlers(context_switcher)
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llm.register_function("respond_with_apple", handlers.respond_with_apple)
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llm.register_function("respond_with_banana", handlers.respond_with_banana)
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llm.register_function("respond_with_orange", handlers.respond_with_oranges)
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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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