202 lines
7.7 KiB
Python
202 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 os
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from typing import override
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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, LLMTextFrame
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from pipecat.observers.loggers.debug_log_observer import DebugLogObserver, FrameEndpoint
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from pipecat.observers.loggers.llm_log_observer import LLMLogObserver
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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 (
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OpenAILLMContext,
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OpenAILLMContextFrame,
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)
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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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"""Manages racing between two LLMs - only allows frames from the first LLM to respond."""
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def __init__(self) -> None:
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super().__init__()
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self._current_llm_name = None
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def set_llm_name(self, name: str):
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"""Set the name of the LLM this processor instance is handling."""
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self._current_llm_name = name
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@override
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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# Always call super first to handle StartFrame and other system frames
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await super().process_frame(frame, direction)
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if isinstance(frame, LLMTextFrame):
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if not LLMRaceProcessor._response_started:
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# First response wins the race
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LLMRaceProcessor._winning_llm_name = self._current_llm_name
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LLMRaceProcessor._response_started = True
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logger.info(
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f"🏆 [LLM_RACE] {self._current_llm_name} wins the race! Text: '{frame.text}'"
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)
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await self.push_frame(frame, direction)
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elif LLMRaceProcessor._winning_llm_name == self._current_llm_name:
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# Continue allowing frames from winning LLM
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logger.info(f"✅ [LLM_RACE] {self._current_llm_name} continuing: '{frame.text}'")
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await self.push_frame(frame, direction)
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else:
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# Drop frames from losing LLM
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logger.info(
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f"❌ [LLM_RACE] Dropping '{frame.text}' from losing LLM: {self._current_llm_name}"
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)
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else:
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# Pass through all non-LLM frames (including system frames)
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await self.push_frame(frame, direction)
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# Class variables to share state between instances
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LLMRaceProcessor._winning_llm_name = None
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LLMRaceProcessor._response_started = False
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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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# Make sure both LLMs share the same context - they should both process context frames
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# In a ParallelPipeline, the context frames will be duplicated to both branches
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# Create separate race processors for each LLM to track which one responds first
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race_processor1 = LLMRaceProcessor()
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race_processor1.set_llm_name("LLM1")
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race_processor2 = LLMRaceProcessor()
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race_processor2.set_llm_name("LLM2")
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# Create parallel LLM branches using ParallelPipeline
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parallel_llms = ParallelPipeline(
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[llm1, race_processor1], # Branch 1: LLM1 -> race processor 1
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[llm2, race_processor2], # Branch 2: LLM2 -> race processor 2
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)
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# Set up debug observers with filtering - only log LLM frames going to TTS
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debug_observer = DebugLogObserver(
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frame_types={LLMTextFrame: (CartesiaTTSService, FrameEndpoint.DESTINATION)}
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)
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llm_observer = LLMLogObserver()
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# Simple pipeline with parallel LLM processing
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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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parallel_llms, # Parallel LLM processing
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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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# observers=[debug_observer, llm_observer],
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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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# Use a simpler approach - add message to context and push a context frame
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messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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# Create a new context with the updated messages
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updated_context = OpenAILLMContext(messages)
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await task.queue_frames([OpenAILLMContextFrame(context=updated_context)])
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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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