127 lines
4.6 KiB
Python
127 lines
4.6 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 sys
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from typing import List
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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.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import TTSSpeakFrame
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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.ai_services import LLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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load_dotenv(override=True)
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async def start_fetch_weather(function_name, llm, context):
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"""Push a frame to the LLM; this is handy when the LLM response might take a while."""
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await llm.push_frame(TTSSpeakFrame("Let me check on that."))
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logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
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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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class MultimodalWeatherBot:
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"""Generic base class for setting up and running an LLM-powered bot."""
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def __init__(self, llm: LLMService):
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"""Initialize the base handler with a specific LLM."""
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self.llm = llm
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@staticmethod
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def tools() -> ToolsSchema:
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weather_function = FunctionSchema(
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name="get_current_weather",
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description="Get the current weather",
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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 user's location.",
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},
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},
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required=["location"],
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)
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return ToolsSchema(standard_tools=[weather_function])
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async def run(self):
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"""Set up and start the processing pipeline."""
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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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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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# 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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self.llm.register_function(
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None, fetch_weather_from_api, start_callback=start_fetch_weather
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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 report the weather in any location in the universe. Respond concisely. Your response will be turned into speech so use only simple words and punctuation.",
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},
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{"role": "user", "content": " Start the conversation by introducing yourself."},
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]
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context = OpenAILLMContext(messages, MultimodalWeatherBot.tools())
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context_aggregator = self.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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self.llm,
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transport.output(),
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context_aggregator.assistant(),
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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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allow_interruptions=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 transport.capture_participant_transcription(participant["id"])
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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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