bff741a647
Pass realtime_service_mode=RealtimeServiceModeConfig() through every realtime LLM service example (base, async-tool, video, text-output, persistent-context, update-settings, MCP) so context aggregation uses the new realtime-mode semantics instead of relying on local VAD as a workaround. Where examples previously wired SileroVADAnalyzer into LLMUserAggregatorParams to coax turn frames out of services that don't emit them server-side (AWS Nova Sonic, Ultravox, Gemini Live), the local VAD is now removed. realtime_service_mode keeps context writes correct without it, and the Phase 1.5 server-side InterruptionFrame fixes for Nova Sonic and Ultravox keep the bot from talking past the user when they barge in. Transcript-logging event handlers move from on_user_turn_stopped / on_assistant_turn_stopped to on_user_message_added / on_assistant_message_added, which carry the finalized text in realtime mode (the turn-stopped events fire before the message is finalized, so their `content` is None in that mode). For services that don't emit user-turn frames (Gemini Live, AWS Nova Sonic, Ultravox) the example now carries a Tier 1 comment block that spells out which downstream processors won't activate, how to add local VAD if needed, and the caveat that locally-generated turn boundaries are a heuristic that may diverge from server-side ground truth. Adds examples/realtime/realtime-openai-local-vad.py, a new variant of the OpenAI Realtime example that disables OpenAI's server-side turn detection and drives turn boundaries locally — useful when you want a turn analyzer like LocalSmartTurnV3 to decide when the user is done speaking. Server-emitted turn frames are still preferred when available. The Gemini Live local-VAD variant already existed; it's been updated in place rather than rewritten.
116 lines
3.5 KiB
Python
116 lines
3.5 KiB
Python
#
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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 asyncio
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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.frames.frames import LLMRunFrame, LLMUpdateSettingsFrame
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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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RealtimeServiceModeConfig,
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)
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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.google.gemini_live.llm import GeminiLiveLLMService
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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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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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llm = GeminiLiveLLMService(
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api_key=os.environ["GOOGLE_API_KEY"],
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settings=GeminiLiveLLMService.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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realtime_service_mode=RealtimeServiceModeConfig(),
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)
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pipeline = Pipeline(
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[
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transport.input(),
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user_aggregator,
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llm,
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transport.output(),
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assistant_aggregator,
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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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)
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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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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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await asyncio.sleep(10)
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logger.info("Updating Gemini Live LLM settings: temperature=0.1")
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await task.queue_frame(
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LLMUpdateSettingsFrame(delta=GeminiLiveLLMService.Settings(temperature=0.1))
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)
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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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