example: hedge llms
This commit is contained in:
James Hush
211
examples/foundational/45-llm-hedge.py
Normal file
211
examples/foundational/45-llm-hedge.py
Normal file
@@ -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()
|
||||
Reference in New Issue
Block a user