147 lines
4.4 KiB
Python
147 lines
4.4 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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"""Pipecat Cloud-compatible bot example.
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Transports are Daily or SmallWebRTC."""
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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.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.openai_llm_context import OpenAILLMContext
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from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
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from pipecat.runner.cloud import SmallWebRTCSessionArguments
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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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try:
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from pipecatcloud.agent import DailySessionArguments
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except ImportError:
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raise ImportError(
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"pipecatcloud package is required for cloud-compatible bots. "
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"Install with: pip install pipecat-ai[[pipecatcloud]]"
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)
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load_dotenv(override=True)
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# Check if we're running locally
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IS_LOCAL_RUN = os.environ.get("LOCAL_RUN", "0") == "1"
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async def run_bot(transport):
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"""Main bot logic that works with any transport."""
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logger.info(f"Starting bot")
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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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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 friendly AI assistant. Respond naturally and keep your answers conversational.",
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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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rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
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pipeline = Pipeline(
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[
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transport.input(),
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rtvi,
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stt,
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context_aggregator.user(),
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llm,
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tts,
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transport.output(),
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context_aggregator.assistant(),
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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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enable_metrics=True,
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enable_usage_metrics=True,
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),
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observers=[RTVIObserver(rtvi)],
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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("Client connected")
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messages.append({"role": "system", "content": "Say hello and briefly introduce yourself."})
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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("Client disconnected")
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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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async def bot(session_args: DailySessionArguments | SmallWebRTCSessionArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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if isinstance(session_args, DailySessionArguments):
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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if not IS_LOCAL_RUN:
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from pipecat.audio.filters.krisp_filter import KrispFilter
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transport = DailyTransport(
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session_args.room_url,
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session_args.token,
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"Pipecat Bot",
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params=DailyParams(
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audio_in_enabled=True,
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audio_in_filter=None
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if IS_LOCAL_RUN
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else KrispFilter(), # Only use Krisp in production
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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)
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elif isinstance(session_args, SmallWebRTCSessionArguments):
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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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transport = SmallWebRTCTransport(
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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_analyzer=SileroVADAnalyzer(),
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),
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webrtc_connection=session_args.webrtc_connection,
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)
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await run_bot(transport)
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if __name__ == "__main__":
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from pipecat.runner.cloud import main
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main()
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