212 lines
7.9 KiB
Python
212 lines
7.9 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 os
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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 EndFrame, EndTaskFrame, TextFrame, TTSSpeakFrame
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from pipecat.pipeline.parallel_pipeline import ParallelPipeline
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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.aggregators.gated_openai_llm_context import GatedOpenAILLMContextAggregator
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.processors.filters.null_filter import NullFilter
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from pipecat.processors.filters.wake_notifier_filter import WakeNotifierFilter
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.processors.user_idle_processor import UserIdleProcessor
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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.openai.llm import OpenAILLMService
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from pipecat.sync.event_notifier import EventNotifier
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from pipecat.transports.base_transport import TransportParams
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from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
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from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
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load_dotenv(override=True)
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class KickParticipantProcessor(FrameProcessor):
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"""This processor will kick the participant if they say something inappropriate.
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This is a simple example of how to use the LLM to moderate
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the conversation. In this case we are using the OpenAI LLM to determine if
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the user is saying something inappropriate.
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"""
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def __init__(self):
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"""Initialize the processor."""
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super().__init__()
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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, TextFrame) and frame.text == "YES":
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await self.push_frame(
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TTSSpeakFrame(
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"You are being kicked from the call because of content moderation. Have a nice day!"
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)
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)
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# Signal that the task should end after processing this frame
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await self.push_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
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await self.push_frame(frame, direction)
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async def run_bot(webrtc_connection: SmallWebRTCConnection):
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logger.info(f"Starting bot")
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transport = SmallWebRTCTransport(
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webrtc_connection=webrtc_connection,
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params=TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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vad_audio_passthrough=True,
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),
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)
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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# This is the LLM that will be used to detect if the user has said
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# something inappropriate.
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moderator_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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moderator_messages = [
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{
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"role": "system",
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"content": """
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You are a helpful LLM that will be used to moderate a conversation
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between a user and an assistant. Your goal is to determine if the user
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is saying something inappropriate. You will be given the user
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transcript and you will have to determine if the user is saying
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something inappropriate. If you think the user is saying something
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inappropriate please respond with "YES". If you think the user is
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saying something appropriate please respond with "NO". Examples of inappropriate
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content are: hate speech, racism, sexism, bullying, harassment,
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violence, self-harm, and any other content that violates the
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community guidelines.
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""",
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},
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]
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moderator_context = OpenAILLMContext(moderator_messages)
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moderator_context_aggregator = moderator_llm.create_context_aggregator(moderator_context)
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# This is the regular LLM.
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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messages = [
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{
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"role": "system",
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"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
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},
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]
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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# We have instructed the LLM to return 'YES' if it thinks the user
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# completed a sentence. So, if it's 'YES' we will return true in this
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# predicate which will wake up the notifier.
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async def wake_check_filter(frame):
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return frame.text == "YES"
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# This is a notifier that we use to synchronize the two LLMs.
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notifier = EventNotifier()
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# This a filter that will wake up the notifier if the given predicate
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# (wake_check_filter) returns true.
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completeness_check = WakeNotifierFilter(notifier, types=(TextFrame,), filter=wake_check_filter)
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# This processor keeps the last context and will let it through once the
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# notifier is woken up. We start with the gate open because we send an
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# initial context frame to start the conversation.
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gated_context_aggregator = GatedOpenAILLMContextAggregator(notifier=notifier, start_open=True)
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# Notify if the user hasn't said anything.
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async def user_idle_notifier(frame):
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await notifier.notify()
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# Sometimes the LLM will fail detecting if a user should be
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# moderated, this will wake up the notifier if that happens.
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user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=3.0)
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kick_participant = KickParticipantProcessor()
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# The ParallePipeline input are the user transcripts. We have two
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# contexts. The first one will be used to determine if the user is
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# moderated and if so the notifier will be woken up. The second
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# context is simply the regular context but it's gated waiting for the
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# notifier to be woken up.
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt,
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ParallelPipeline(
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[
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moderator_context_aggregator.user(),
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moderator_llm,
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kick_participant,
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completeness_check,
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NullFilter(),
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],
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[context_aggregator.user(), gated_context_aggregator, llm],
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),
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user_idle,
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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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params=PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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report_only_initial_ttfb=True,
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),
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation.
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messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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@transport.event_handler("on_client_closed")
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async def on_client_closed(transport, client):
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logger.info(f"Client closed connection")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=False)
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await runner.run(task)
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if __name__ == "__main__":
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from run import main
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main()
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