# # Copyright (c) 2024–2025, Daily # # SPDX-License-Identifier: BSD 2-Clause License # import os from dotenv import load_dotenv from loguru import logger from simli import SimliConfig from pipecat.audio.vad.silero import SileroVADAnalyzer from pipecat.frames.frames import LLMRunFrame 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.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.services.simli.video import SimliVideoService from pipecat.transports.base_transport import BaseTransport, TransportParams from pipecat.transports.daily.transport 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, video_out_enabled=True, video_out_is_live=True, video_out_width=512, video_out_height=512, vad_analyzer=SileroVADAnalyzer(), ), "webrtc": lambda: TransportParams( audio_in_enabled=True, audio_out_enabled=True, video_out_enabled=True, video_out_is_live=True, video_out_width=512, video_out_height=512, vad_analyzer=SileroVADAnalyzer(), ), } async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): logger.info(f"Starting bot") stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY")) tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY"), voice_id="a167e0f3-df7e-4d52-a9c3-f949145efdab", ) simli_ai = SimliVideoService( SimliConfig(os.getenv("SIMLI_API_KEY"), os.getenv("SIMLI_FACE_ID")), ) llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini") 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.", }, ] context = OpenAILLMContext(messages) context_aggregator = llm.create_context_aggregator(context) pipeline = Pipeline( [ transport.input(), stt, context_aggregator.user(), llm, tts, simli_ai, transport.output(), context_aggregator.assistant(), ] ) 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") # Start conversation - empty prompt to let LLM follow system instructions await task.queue_frames([LLMRunFrame()]) @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()