Creating a new example for async stream using Google.
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filipi87
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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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"""Example: async function call with intermediate updates.
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The ``track_current_location`` tool simulates a GPS tracker reporting the
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device's position during a road trip from San Francisco to San Diego. It
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sends two intermediate updates (via ``params.result_callback`` with
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``is_final=False``) as the vehicle passes through cities along the way, then
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delivers the final destination (via ``params.result_callback``). Each update
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returns the same structure with a different city:
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Update 1 – {gps, city: "San Francisco"} ← trip start
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Update 2 – {gps, city: "Los Angeles"} ← passing through
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Final – {gps, city: "San Diego"} ← destination reached
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Because the function is registered with ``cancel_on_interruption=False``, the
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LLM can keep talking while the trip is in progress; each position update
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arrives as a developer message so the LLM can narrate the journey to the user.
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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.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 (
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FunctionCallResultProperties,
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LLMRunFrame,
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TTSSpeakFrame,
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)
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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.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.google.llm import GoogleLLMService
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from pipecat.services.llm_service import FunctionCallParams
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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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async def track_current_location(params: FunctionCallParams):
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"""Simulate a GPS tracker reporting position during a road trip.
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Step 1 – San Francisco (trip start) (update)
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Step 2 – Los Angeles (passing through) (update)
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Step 3 – San Diego (destination) (final result)
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"""
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# First update: initial city estimate.
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gps = {"lat": 37.7310, "lng": -122.4527}
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await params.result_callback(
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{"gps": gps, "city": "San Francisco"},
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properties=FunctionCallResultProperties(is_final=False),
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)
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# Second update: revised city estimate.
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await asyncio.sleep(10)
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gps = {"lat": 33.96003, "lng": -118.40639}
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await params.result_callback(
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{"gps": gps, "city": "Los Angeles"},
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properties=FunctionCallResultProperties(is_final=False),
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)
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# Final result: confirmed city.
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await asyncio.sleep(10)
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gps = {"lat": 32.743569, "lng": -117.20466}
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await params.result_callback({"gps": gps, "city": "San Diego"})
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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 = GoogleLLMService(
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api_key=os.getenv("GOOGLE_API_KEY"),
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settings=GoogleLLMService.Settings(
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system_instruction=(
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"You are a helpful assistant in a voice conversation. "
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"Your responses will be spoken aloud, so avoid emojis, bullet points, or other "
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"formatting that can't be spoken. "
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"You have access to a function that starts tracking the user's location and "
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"provides regular updates on it. When you receive the final location, tell the user "
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"the destination has been reached."
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),
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),
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)
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# cancel_on_interruption=False makes this an async function call: the LLM
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# continues the conversation immediately and receives updates/result later.
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llm.register_function(
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"track_current_location",
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track_current_location,
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cancel_on_interruption=False,
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timeout_secs=30,
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)
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@llm.event_handler("on_function_calls_started")
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async def on_function_calls_started(service, function_calls):
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await tts.queue_frame(TTSSpeakFrame("Sure, tracking your location now."))
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location_function = FunctionSchema(
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name="track_current_location",
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description="Start tracking the user's current GPS location, reporting position updates until the user reaches their destination.",
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properties={},
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required=[],
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)
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tools = ToolsSchema(standard_tools=[location_function])
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context = LLMContext(tools=tools)
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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(),
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stt,
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user_aggregator,
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llm,
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tts,
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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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)
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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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