Rename example files to prepend parent folder name, preventing package shadowing
Example files like openai.py shadow installed packages when Python adds the script directory to sys.path. Prepend the parent folder name to each example file (e.g. openai.py -> function-calling-openai.py). Also split thinking-and-mcp/ into separate mcp/ and thinking/ directories.
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Mark Backman
197
examples/realtime/realtime-gemini-live-graceful-end.py
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197
examples/realtime/realtime-gemini-live-graceful-end.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 datetime import datetime
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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 AdapterType, ToolsSchema
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from pipecat.frames.frames import EndTaskFrame, LLMRunFrame
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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 LLMContextAggregatorPair
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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.google.gemini_live.llm import GeminiLiveLLMService
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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 fetch_weather_from_api(params: FunctionCallParams):
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temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
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await params.result_callback(
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{
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"conditions": "nice",
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"temperature": temperature,
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"format": params.arguments["format"],
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"timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
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}
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)
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async def fetch_restaurant_recommendation(params: FunctionCallParams):
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await params.result_callback({"name": "The Golden Dragon"})
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async def end_conversation(params: FunctionCallParams):
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await params.result_callback({"success": True})
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await params.llm.push_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
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# NOTE: we can ask the model to say something *after* the call to
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# end_conversation because GeminiLiveLLMService defers processing EndFrames
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# until after the bot finishes its current turn. With Gemini 3.1 Flash Live,
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# the model won't reliably report ending its turn until after it says something
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# following the tool call, which is why the system instruction is structured
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# the way it is.
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system_instruction = """
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You are a helpful assistant who can answer questions and use tools.
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You have three tools available to you:
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1. get_current_weather: Use this tool to get the current weather in a specific location.
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2. get_restaurant_recommendation: Use this tool to get a restaurant recommendation in a specific location.
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3. end_conversation: Use this tool to gracefully end the conversation.
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After you've responded to the user three times, do the following:
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1. Politely let them know that that's all the time you have today (but don't say "goodbye" yet).
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2. Then immediately call the end_conversation function. *DO NOT FORGET TO DO THIS STEP.*
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3. After the tool reports success, say goodbye.
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"""
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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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weather_function = FunctionSchema(
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name="get_current_weather",
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description="Get the current weather",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the user's location.",
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},
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},
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required=["location", "format"],
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)
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restaurant_function = FunctionSchema(
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name="get_restaurant_recommendation",
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description="Get a restaurant recommendation",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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},
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required=["location"],
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)
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end_conversation_function = FunctionSchema(
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name="end_conversation",
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description="Gracefully end the conversation",
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properties={},
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required=[],
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)
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search_tool = {"google_search": {}}
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tools = ToolsSchema(
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standard_tools=[weather_function, restaurant_function, end_conversation_function],
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custom_tools={AdapterType.GEMINI: [search_tool]},
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)
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llm = GeminiLiveLLMService(
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api_key=os.getenv("GOOGLE_API_KEY"),
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settings=GeminiLiveLLMService.Settings(
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system_instruction=system_instruction,
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),
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tools=tools,
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)
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llm.register_function("get_current_weather", fetch_weather_from_api)
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llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
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llm.register_function("end_conversation", end_conversation)
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context = LLMContext(
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[{"role": "developer", "content": "Say hello."}],
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)
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# Server-side VAD is enabled by default; no local VAD is added.
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(context)
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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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# Kick off the conversation.
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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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