201 lines
7.8 KiB
Python
201 lines
7.8 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 argparse
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import os
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import random
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import sys
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
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from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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from pipecat.frames.frames import LLMRunFrame, ThoughtTranscriptionMessage, TranscriptionMessage
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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.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
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from pipecat.processors.transcript_processor import TranscriptProcessor
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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.anthropic.llm import AnthropicLLMService
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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.google.llm import GoogleLLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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# LLM provider constants
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LLM_ANTHROPIC = "anthropic"
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LLM_GOOGLE = "google"
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LLM_DEFAULT = LLM_GOOGLE
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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(params=VADParams(stop_secs=0.2)),
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turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
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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(params=VADParams(stop_secs=0.2)),
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turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
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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(params=VADParams(stop_secs=0.2)),
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turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
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),
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}
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async def run_bot(
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transport: BaseTransport, runner_args: RunnerArguments, llm_provider: str = LLM_DEFAULT
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):
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logger.info(f"Starting bot with {llm_provider.capitalize()} LLM")
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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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if llm_provider == LLM_ANTHROPIC:
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llm = AnthropicLLMService(
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api_key=os.getenv("ANTHROPIC_API_KEY"),
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params=AnthropicLLMService.InputParams(
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thinking=AnthropicLLMService.ThinkingConfig(type="enabled", budget_tokens=2048)
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),
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)
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elif llm_provider == LLM_GOOGLE:
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llm = GoogleLLMService(
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api_key=os.getenv("GOOGLE_API_KEY"),
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# model="gemini-3-pro-preview", # A more powerful reasoning model, but slower
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params=GoogleLLMService.InputParams(
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thinking=GoogleLLMService.ThinkingConfig(
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# thinking_level="low", # Use this field instead of thinking_budget for Gemini 3 Pro. Defaults to "high".
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thinking_budget=-1, # Dynamic thinking
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include_thoughts=True,
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)
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),
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)
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else:
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raise ValueError(f"Unsupported LLM provider: {llm_provider}")
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transcript = TranscriptProcessor()
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messages = [
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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 spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
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},
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]
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context = LLMContext(messages)
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context_aggregator = LLMContextAggregatorPair(context)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt,
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transcript.user(), # User transcripts
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context_aggregator.user(), # User responses
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llm, # LLM
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transcript.thought(), # Thought transcripts
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tts, # TTS
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transport.output(), # Transport bot output
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transcript.assistant(), # Assistant transcripts
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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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# Choose a random prompt to demonstrate thinking capabilities.
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# These prompts were chosen from Google and Anthropic docs.
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thinking_prompt_1 = "Analogize photosynthesis and growing up."
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thinking_prompt_2 = "Compare and contrast electric cars and hybrid cars."
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thinking_prompt_3 = "Are there an infinite number of prime numbers such that n mod 4 == 3?"
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selected_prompt = random.choice([thinking_prompt_1, thinking_prompt_2, thinking_prompt_3])
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# Kick off the conversation.
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messages.append({"role": "user", "content": selected_prompt})
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await task.queue_frames([LLMRunFrame()])
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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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# Register event handler for transcript updates
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@transcript.event_handler("on_transcript_update")
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async def on_transcript_update(processor, frame):
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for msg in frame.messages:
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if isinstance(msg, (ThoughtTranscriptionMessage, TranscriptionMessage)):
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timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
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role = "THOUGHT" if isinstance(msg, ThoughtTranscriptionMessage) else msg.role
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logger.info(f"Transcript: {timestamp}{role}: {msg.content}")
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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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# Get llm_provider from module attribute set in __main__
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llm_provider = getattr(sys.modules[__name__], "llm_provider", LLM_DEFAULT)
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args, llm_provider)
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if __name__ == "__main__":
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# Parse custom arguments before calling runner main()
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parser = argparse.ArgumentParser(description="Thinking LLM Bot")
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parser.add_argument(
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"--llm",
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type=str,
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choices=[LLM_ANTHROPIC, LLM_GOOGLE],
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default=LLM_DEFAULT,
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help=f"LLM provider to use (default: {LLM_DEFAULT})",
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)
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# Parse only known args to allow runner's main() to handle its own args
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args, remaining = parser.parse_known_args()
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# Store the llm_provider in sys.modules for bot() function to access
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sys.modules[__name__].llm_provider = args.llm
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# Restore sys.argv with remaining args for runner's main()
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sys.argv[1:] = remaining
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from pipecat.runner.run import main
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main()
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