143 lines
4.4 KiB
Python
143 lines
4.4 KiB
Python
#
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# Copyright (c) 2024, 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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from datetime import datetime
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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 runner import configure
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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.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.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
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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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temperature = 75 if args["format"] == "fahrenheit" else 24
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await result_callback(
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{
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"conditions": "nice",
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"temperature": temperature,
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"format": args["format"],
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"timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
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}
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)
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tools = [
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{
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"function_declarations": [
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{
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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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]
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system_instruction = """
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You are a helpful assistant who can answer questions and use tools.
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You have a tool called "get_current_weather" that can be used to get the current weather. If the user asks
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for the weather, call this function.
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"""
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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transport = DailyTransport(
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room_url,
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token,
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"Respond bot",
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DailyParams(
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audio_in_sample_rate=16000,
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audio_out_sample_rate=24000,
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audio_out_enabled=True,
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vad_enabled=True,
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vad_audio_passthrough=True,
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# set stop_secs to something roughly similar to the internal setting
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# of the Multimodal Live api, just to align events. This doesn't really
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# matter because we can only use the Multimodal Live API's phrase
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# endpointing, for now.
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
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),
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)
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llm = GeminiMultimodalLiveLLMService(
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api_key=os.getenv("GOOGLE_API_KEY"),
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system_instruction=system_instruction,
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tools=tools,
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)
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llm.register_function("get_current_weather", fetch_weather_from_api)
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context = OpenAILLMContext(
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[{"role": "user", "content": "Say hello."}],
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)
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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(),
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context_aggregator.user(),
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llm,
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context_aggregator.assistant(),
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transport.output(),
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]
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)
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task = PipelineTask(
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pipeline,
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PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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)
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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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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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