141 lines
5.0 KiB
Python
141 lines
5.0 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 asyncio
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import os
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import sys
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import aiohttp
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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 ChatCompletionToolParam
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from runner import configure
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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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 OpenAILLMContext
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from pipecat.processors.filters.stt_mute_filter import STTMuteConfig, STTMuteFilter, STTMuteStrategy
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from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
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# Add a delay to test interruption during function calls
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logger.info("Weather API call starting...")
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await asyncio.sleep(5) # 5-second delay
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logger.info("Weather API call completed")
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await result_callback({"conditions": "nice", "temperature": "75"})
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, _) = await configure(session)
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transport = DailyTransport(
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room_url,
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None,
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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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vad_audio_passthrough=True,
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),
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)
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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# Configure the mute processor with both strategies
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stt_mute_processor = STTMuteFilter(
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config=STTMuteConfig(
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strategies={
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STTMuteStrategy.MUTE_UNTIL_FIRST_BOT_COMPLETE,
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STTMuteStrategy.FUNCTION_CALL,
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}
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),
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)
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tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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llm.register_function("get_current_weather", fetch_weather_from_api)
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tools = [
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ChatCompletionToolParam(
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type="function",
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function={
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"name": "get_current_weather",
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"description": "Get the current weather",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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},
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"required": ["location", "format"],
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},
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},
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)
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]
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messages = [
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{
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"role": "system",
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"content": "You are a helpful assistant who can check the weather. Always check the weather when a location is mentioned. Respond concisely and naturally. Your output will be converted to audio so use only simple words and punctuation.",
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},
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]
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context = OpenAILLMContext(messages, tools)
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context_aggregator = llm.create_context_aggregator(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_mute_processor, # Add the mute processor before STT
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stt, # STT
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context_aggregator.user(), # User responses
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llm, # LLM
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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(pipeline, params=PipelineParams(allow_interruptions=True))
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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# Kick off the conversation with a weather-related prompt
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messages.append(
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{
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"role": "system",
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"content": "Ask the user what city they'd like to know the weather for.",
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}
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)
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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