Split features/ into audio/, observability/, and rag/ subfolders
Extract focused example groups from the catch-all features/ folder: - audio/: audio recording, background sound, sound effects - observability/: observer, heartbeats, sentry metrics - rag/: mem0, gemini-rag, gemini grounding metadata Update README to document the new folders.
This commit is contained in:
Mark Backman
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examples/observability/observer.py
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185
examples/observability/observer.py
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#
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# Copyright (c) 2024-2026, 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 os
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import (
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BotStartedSpeakingFrame,
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BotStoppedSpeakingFrame,
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EndFrame,
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InterruptionFrame,
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LLMRunFrame,
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TTSTextFrame,
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UserStartedSpeakingFrame,
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)
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from pipecat.observers.base_observer import BaseObserver, FramePushed
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from pipecat.observers.loggers.debug_log_observer import DebugLogObserver, FrameEndpoint
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from pipecat.observers.loggers.llm_log_observer import LLMLogObserver
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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.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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from pipecat.processors.frame_processor import FrameDirection
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.base_input import BaseInputTransport
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from pipecat.transports.base_output import BaseOutputTransport
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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class CustomObserver(BaseObserver):
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"""Observer to log interruptions and bot speaking events to the console.
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Logs all frame instances of:
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- InterruptionFrame
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- BotStartedSpeakingFrame
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- BotStoppedSpeakingFrame
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This allows you to see the frame flow from processor to processor through the pipeline for these frames.
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Log format: [EVENT TYPE]: [source processor] → [destination processor] at [timestamp]s
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"""
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async def on_push_frame(self, data: FramePushed):
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src = data.source
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dst = data.destination
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frame = data.frame
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direction = data.direction
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timestamp = data.timestamp
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# Convert timestamp to seconds for readability
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time_sec = timestamp / 1_000_000_000
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# Create direction arrow
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arrow = "→" if direction == FrameDirection.DOWNSTREAM else "←"
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if isinstance(frame, InterruptionFrame) and isinstance(src, BaseOutputTransport):
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logger.info(f"⚡ INTERRUPTION START: {src} {arrow} {dst} at {time_sec:.2f}s")
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elif isinstance(frame, BotStartedSpeakingFrame):
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logger.info(f"🤖 BOT START SPEAKING: {src} {arrow} {dst} at {time_sec:.2f}s")
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elif isinstance(frame, BotStoppedSpeakingFrame):
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logger.info(f"🤖 BOT STOP SPEAKING: {src} {arrow} {dst} at {time_sec:.2f}s")
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# We use lambdas to defer transport parameter creation until the transport
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# type is selected at runtime.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot")
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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settings=CartesiaTTSService.Settings(
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voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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),
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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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settings=OpenAILLMService.Settings(
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system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
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),
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)
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context = LLMContext()
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
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)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt,
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user_aggregator, # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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assistant_aggregator, # Assistant spoken responses
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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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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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observers=[
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CustomObserver(),
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LLMLogObserver(),
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DebugLogObserver(
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frame_types={
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TTSTextFrame: (BaseOutputTransport, FrameEndpoint.SOURCE),
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UserStartedSpeakingFrame: (BaseInputTransport, FrameEndpoint.SOURCE),
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EndFrame: None,
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}
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),
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],
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation.
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context.add_message(
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{"role": "developer", "content": "Please introduce yourself to the user."}
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)
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await task.queue_frames([LLMRunFrame()])
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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