From c439d7981f7bcfcc8ef042351764dffaea1cad92 Mon Sep 17 00:00:00 2001 From: James Hush Date: Fri, 5 Sep 2025 16:13:46 +0800 Subject: [PATCH] example: hedge llms --- examples/foundational/45-llm-hedge.py | 211 ++++++++++++++++++++++++++ 1 file changed, 211 insertions(+) create mode 100644 examples/foundational/45-llm-hedge.py diff --git a/examples/foundational/45-llm-hedge.py b/examples/foundational/45-llm-hedge.py new file mode 100644 index 000000000..e5d1715df --- /dev/null +++ b/examples/foundational/45-llm-hedge.py @@ -0,0 +1,211 @@ +# +# 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 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 = [ + { + "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()