examples: added sentry-metrics example

This commit is contained in:
Aleix Conchillo Flaqué
2025-02-06 10:43:10 -08:00
parent c5d5ca8232
commit c4be07693f
9 changed files with 524 additions and 0 deletions

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#
# Copyright (c) 20242025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
import aiohttp
import sentry_sdk
from dotenv import load_dotenv
from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
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.processors.metrics.sentry import SentryMetrics
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
async def main():
async with aiohttp.ClientSession() as session:
(room_url, token) = await configure(session)
transport = DailyTransport(
room_url,
token,
"Chatbot",
DailyParams(
audio_out_enabled=True,
audio_in_enabled=True,
camera_out_enabled=False,
vad_enabled=True,
vad_audio_passthrough=True,
vad_analyzer=SileroVADAnalyzer(),
transcription_enabled=True,
),
)
# Initialize Sentry
sentry_sdk.init(
dsn="your-project-dsn",
traces_sample_rate=1.0,
)
tts = ElevenLabsTTSService(
api_key=os.getenv("ELEVENLABS_API_KEY"),
voice_id="cgSgspJ2msm6clMCkdW9",
metrics=SentryMetrics(),
)
llm = OpenAILLMService(
api_key=os.getenv("OPENAI_API_KEY"),
model="gpt-4o",
metrics=SentryMetrics(),
)
messages = [
{
"role": "system",
"content": "You are Chatbot, a friendly, helpful robot. 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, but keep your responses brief. Start by introducing yourself.",
},
]
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # microphone
context_aggregator.user(),
llm,
tts,
transport.output(),
context_aggregator.assistant(),
]
)
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):
await transport.capture_participant_transcription(participant["id"])
await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
print(f"Participant left: {participant}")
await task.cancel()
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())