139 lines
4.5 KiB
Python
139 lines
4.5 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 aiohttp
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import os
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import sys
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from pipecat.frames.frames import TextFrame
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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 PipelineTask
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from pipecat.processors.logger import FrameLogger
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.openai import OpenAILLMContext, OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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from pipecat.vad.silero import SileroVADAnalyzer
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from openai.types.chat import ChatCompletionToolParam
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from runner import configure
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from loguru import logger
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from dotenv import load_dotenv
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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 start_fetch_weather(function_name, llm, context):
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await llm.push_frame(TextFrame("Let me check on that."))
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async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
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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, 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_out_enabled=True,
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transcription_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer()
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)
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)
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tts = ElevenLabsTTSService(
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aiohttp_session=session,
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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model="gpt-4o")
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# Register a function_name of None to get all functions
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# sent to the same callback with an additional function_name parameter.
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llm.register_function(
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None,
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fetch_weather_from_api,
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start_callback=start_fetch_weather)
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fl_in = FrameLogger("Inner")
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fl_out = FrameLogger("Outer")
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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": [
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"celsius",
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"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": [
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"location",
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"format"],
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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 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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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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fl_in,
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transport.input(),
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context_aggregator.user(),
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llm,
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fl_out,
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tts,
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transport.output(),
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context_aggregator.assistant(),
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])
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task = PipelineTask(pipeline)
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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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transport.capture_participant_transcription(participant["id"])
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# Kick off the conversation.
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await tts.say("Hi! Ask me about the weather in San Francisco.")
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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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