119 lines
3.9 KiB
Python
119 lines
3.9 KiB
Python
#
|
|
# Copyright (c) 2024, Daily
|
|
#
|
|
# SPDX-License-Identifier: BSD 2-Clause License
|
|
#
|
|
|
|
import asyncio
|
|
import aiohttp
|
|
import os
|
|
import sys
|
|
|
|
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.services.cartesia import CartesiaTTSService
|
|
from pipecat.services.anthropic import AnthropicLLMService
|
|
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
|
from pipecat.vad.silero import SileroVADAnalyzer
|
|
|
|
from runner import configure
|
|
|
|
from loguru import logger
|
|
|
|
from dotenv import load_dotenv
|
|
|
|
load_dotenv(override=True)
|
|
|
|
logger.remove(0)
|
|
logger.add(sys.stderr, level="DEBUG")
|
|
|
|
|
|
async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
|
|
location = arguments["location"]
|
|
await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
|
|
|
|
|
|
async def main():
|
|
async with aiohttp.ClientSession() as session:
|
|
(room_url, token) = await configure(session)
|
|
|
|
transport = DailyTransport(
|
|
room_url,
|
|
token,
|
|
"Respond bot",
|
|
DailyParams(
|
|
audio_out_enabled=True,
|
|
transcription_enabled=True,
|
|
vad_enabled=True,
|
|
vad_analyzer=SileroVADAnalyzer(),
|
|
),
|
|
)
|
|
|
|
tts = CartesiaTTSService(
|
|
api_key=os.getenv("CARTESIA_API_KEY"),
|
|
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
|
)
|
|
|
|
llm = AnthropicLLMService(
|
|
api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20240620"
|
|
)
|
|
llm.register_function("get_weather", get_weather)
|
|
|
|
tools = [
|
|
{
|
|
"name": "get_weather",
|
|
"description": "Get the current weather in a given location",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {
|
|
"location": {
|
|
"type": "string",
|
|
"description": "The city and state, e.g. San Francisco, CA",
|
|
}
|
|
},
|
|
"required": ["location"],
|
|
},
|
|
}
|
|
]
|
|
|
|
# todo: test with very short initial user message
|
|
|
|
# messages = [{"role": "system",
|
|
# "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."},
|
|
# {"role": "user",
|
|
# "content": " Start the conversation by introducing yourself."}]
|
|
|
|
messages = [{"role": "user", "content": "Say 'hello' to start the conversation."}]
|
|
|
|
context = OpenAILLMContext(messages, tools)
|
|
context_aggregator = llm.create_context_aggregator(context)
|
|
|
|
pipeline = Pipeline(
|
|
[
|
|
transport.input(), # Transport user input
|
|
context_aggregator.user(), # User spoken responses
|
|
llm, # LLM
|
|
tts, # TTS
|
|
transport.output(), # Transport bot output
|
|
context_aggregator.assistant(), # Assistant spoken responses and tool context
|
|
]
|
|
)
|
|
|
|
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))
|
|
|
|
@transport.event_handler("on_first_participant_joined")
|
|
async def on_first_participant_joined(transport, participant):
|
|
transport.capture_participant_transcription(participant["id"])
|
|
# Kick off the conversation.
|
|
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
|
|
|
runner = PipelineRunner()
|
|
|
|
await runner.run(task)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|