# # Copyright (c) 2024–2025, Daily # # SPDX-License-Identifier: BSD 2-Clause License # import asyncio import os from dotenv import load_dotenv from loguru import logger from openai.types.chat import ChatCompletionMessageParam from pipecat.audio.vad.silero import SileroVADAnalyzer from pipecat.frames.frames import Frame, LLMMessagesFrame from pipecat.pipeline.parallel_pipeline import ParallelPipeline from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.processors.frame_processor import FrameDirection, FrameProcessor from pipecat.runner.types import RunnerArguments from pipecat.runner.utils import create_transport from pipecat.services.cartesia.tts import CartesiaTTSService from pipecat.services.deepgram.stt import DeepgramSTTService from pipecat.services.openai.llm import OpenAILLMService from pipecat.transports.base_transport import BaseTransport, TransportParams from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams from pipecat.transports.services.daily import DailyParams load_dotenv(override=True) # We store functions so objects (e.g. SileroVADAnalyzer) don't get # instantiated. The function will be called when the desired transport gets # selected. transport_params = { "daily": lambda: DailyParams( audio_in_enabled=True, audio_out_enabled=True, vad_analyzer=SileroVADAnalyzer(), ), "twilio": lambda: FastAPIWebsocketParams( audio_in_enabled=True, audio_out_enabled=True, vad_analyzer=SileroVADAnalyzer(), ), "webrtc": lambda: TransportParams( audio_in_enabled=True, audio_out_enabled=True, vad_analyzer=SileroVADAnalyzer(), ), } class LLMRaceProcessor(FrameProcessor): """Processor that sends frames to two LLMs in parallel and uses the first response.""" def __init__(self, llm1: OpenAILLMService, llm2: OpenAILLMService): super().__init__() self._llm1 = llm1 self._llm2 = llm2 self._race_counter = 0 self._active_races = {} # race_id -> winner_name async def process_frame(self, frame, direction): await super().process_frame(frame, direction) # Check if this is a frame we should race through both LLMs if isinstance(frame, LLMMessagesFrame): race_id = self._race_counter self._race_counter += 1 logger.info(f"[LLM_RACE {race_id}] Starting parallel processing") # Create a result collector for this race race_result = asyncio.Event() winning_frames = [] async def llm_runner(llm: OpenAILLMService, name: str): """Run LLM and collect results.""" try: # Create a frame collector class FrameCollector(FrameProcessor): def __init__(self): super().__init__() self.collected_frames = [] async def process_frame(self, frame, direction): self.collected_frames.append(frame) collector = FrameCollector() # Temporarily link LLM to collector llm.link(collector) # Process the frame await llm.process_frame(frame, FrameDirection.DOWNSTREAM) # Check if we won the race if race_id not in self._active_races: self._active_races[race_id] = name winning_frames.extend(collector.collected_frames) race_result.set() logger.info( f"[LLM_RACE {race_id}] {name} WON with {len(collector.collected_frames)} frames!" ) else: logger.info(f"[LLM_RACE {race_id}] {name} lost") except Exception as e: logger.error(f"[LLM_RACE {race_id}] Error in {name}: {e}") # Start both LLMs racing task1 = asyncio.create_task(llm_runner(self._llm1, "LLM1")) task2 = asyncio.create_task(llm_runner(self._llm2, "LLM2")) # Wait for the first one to complete await race_result.wait() # Cancel the slower task task1.cancel() task2.cancel() # Push the winning frames for winning_frame in winning_frames: await self.push_frame(winning_frame, direction) else: # Pass through non-LLM frames await self.push_frame(frame, direction) async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): logger.info(f"Starting bot with parallel LLM racing") stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY", "")) tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady ) # Create two LLM instances for racing llm1 = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY")) llm2 = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY")) messages: list[ChatCompletionMessageParam] = [ { "role": "system", "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.", }, ] # Create shared context for both LLMs context = OpenAILLMContext(messages) context_aggregator = llm1.create_context_aggregator(context) # Create a second context aggregator that shares the same context context_aggregator2 = llm2.create_context_aggregator(context) # Create race processor with both LLMs race_processor = LLMRaceProcessor(llm1, llm2) # Simple pipeline - the race processor handles the parallel LLM execution internally pipeline = Pipeline( [ transport.input(), # Transport user input stt, # Speech to text context_aggregator.user(), # User responses (creates context frames for LLMs) race_processor, # Parallel LLM racing processor tts, # TTS transport.output(), # Transport bot output context_aggregator.assistant(), # Assistant spoken responses ] ) task = PipelineTask( pipeline, params=PipelineParams( enable_metrics=True, enable_usage_metrics=True, ), idle_timeout_secs=runner_args.pipeline_idle_timeout_secs, ) @transport.event_handler("on_client_connected") async def on_client_connected(transport, client): logger.info(f"Client connected") # Kick off the conversation. messages.append({"role": "system", "content": "Please introduce yourself to the user."}) await task.queue_frames([context_aggregator.user().get_context_frame()]) @transport.event_handler("on_client_disconnected") async def on_client_disconnected(transport, client): logger.info(f"Client disconnected") await task.cancel() runner = PipelineRunner(handle_sigint=runner_args.handle_sigint) await runner.run(task) async def bot(runner_args: RunnerArguments): """Main bot entry point compatible with Pipecat Cloud.""" transport = await create_transport(runner_args, transport_params) await run_bot(transport, runner_args) if __name__ == "__main__": from pipecat.runner.run import main main()