212 lines
7.7 KiB
Python
212 lines
7.7 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 asyncio
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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 openai.types.chat import ChatCompletionMessageParam
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import Frame, LLMMessagesFrame
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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.openai_llm_context import OpenAILLMContext
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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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.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
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from pipecat.transports.services.daily import DailyParams
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load_dotenv(override=True)
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# We store functions so objects (e.g. SileroVADAnalyzer) don't get
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# instantiated. The function will be called when the desired transport gets
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# selected.
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transport_params = {
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"daily": lambda: DailyParams(
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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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"twilio": lambda: FastAPIWebsocketParams(
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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": lambda: 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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}
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class LLMRaceProcessor(FrameProcessor):
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"""Processor that sends frames to two LLMs in parallel and uses the first response."""
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def __init__(self, llm1: OpenAILLMService, llm2: OpenAILLMService):
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super().__init__()
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self._llm1 = llm1
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self._llm2 = llm2
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self._race_counter = 0
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self._active_races = {} # race_id -> winner_name
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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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# Check if this is a frame we should race through both LLMs
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if isinstance(frame, LLMMessagesFrame):
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race_id = self._race_counter
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self._race_counter += 1
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logger.info(f"[LLM_RACE {race_id}] Starting parallel processing")
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# Create a result collector for this race
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race_result = asyncio.Event()
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winning_frames = []
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async def llm_runner(llm: OpenAILLMService, name: str):
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"""Run LLM and collect results."""
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try:
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# Create a frame collector
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class FrameCollector(FrameProcessor):
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def __init__(self):
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super().__init__()
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self.collected_frames = []
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async def process_frame(self, frame, direction):
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self.collected_frames.append(frame)
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collector = FrameCollector()
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# Temporarily link LLM to collector
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llm.link(collector)
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# Process the frame
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await llm.process_frame(frame, FrameDirection.DOWNSTREAM)
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# Check if we won the race
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if race_id not in self._active_races:
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self._active_races[race_id] = name
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winning_frames.extend(collector.collected_frames)
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race_result.set()
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logger.info(
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f"[LLM_RACE {race_id}] {name} WON with {len(collector.collected_frames)} frames!"
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)
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else:
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logger.info(f"[LLM_RACE {race_id}] {name} lost")
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except Exception as e:
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logger.error(f"[LLM_RACE {race_id}] Error in {name}: {e}")
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# Start both LLMs racing
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task1 = asyncio.create_task(llm_runner(self._llm1, "LLM1"))
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task2 = asyncio.create_task(llm_runner(self._llm2, "LLM2"))
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# Wait for the first one to complete
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await race_result.wait()
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# Cancel the slower task
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task1.cancel()
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task2.cancel()
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# Push the winning frames
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for winning_frame in winning_frames:
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await self.push_frame(winning_frame, direction)
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else:
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# Pass through non-LLM frames
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await self.push_frame(frame, direction)
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot with parallel LLM racing")
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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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# Create two LLM instances for racing
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llm1 = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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llm2 = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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messages: list[ChatCompletionMessageParam] = [
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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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# Create shared context for both LLMs
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context = OpenAILLMContext(messages)
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context_aggregator = llm1.create_context_aggregator(context)
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# Create a second context aggregator that shares the same context
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context_aggregator2 = llm2.create_context_aggregator(context)
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# Create race processor with both LLMs
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race_processor = LLMRaceProcessor(llm1, llm2)
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# Simple pipeline - the race processor handles the parallel LLM execution internally
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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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context_aggregator.user(), # User responses (creates context frames for LLMs)
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race_processor, # Parallel LLM racing processor
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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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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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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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await task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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