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.
This commit is contained in:
Mark Backman
248
examples/function-calling/function-calling-google.py
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248
examples/function-calling/function-calling-google.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.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 LLMRunFrame, TTSSpeakFrame, UserImageRequestFrame
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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 (
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create_transport,
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get_transport_client_id,
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maybe_capture_participant_camera,
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)
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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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load_dotenv(override=True)
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async def get_weather(params: FunctionCallParams):
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location = params.arguments["location"]
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await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
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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 get_image(params: FunctionCallParams):
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"""Fetch the user image and push it to the LLM.
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When called, this function pushes a UserImageRequestFrame upstream to the
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transport. As a result, the transport will request the user image and push a
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UserImageRawFrame downstream which will be added to the context by the LLM
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assistant aggregator. The result_callback will be invoked once the image is
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retrieved and processed.
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"""
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user_id = params.arguments["user_id"]
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question = params.arguments["question"]
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logger.debug(f"Requesting image with user_id={user_id}, question={question}")
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# Request a user image frame and indicate that it should be added to the
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# context. Also associate it to the function call. Pass the result_callback
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# so it can be invoked when the image is actually retrieved.
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await params.llm.push_frame(
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UserImageRequestFrame(
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user_id=user_id,
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text=question,
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append_to_context=True,
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function_name=params.function_name,
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tool_call_id=params.tool_call_id,
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result_callback=params.result_callback,
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),
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FrameDirection.UPSTREAM,
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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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video_in_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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video_in_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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system_prompt = """\
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You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
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Your response will be turned into speech so use only simple words and punctuation.
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You have access to three tools: get_weather, get_restaurant_recommendation, and get_image.
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You can respond to questions about the weather using the get_weather tool.
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You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
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indicate you should use the get_image tool are:
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- What do you see?
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- What's in the video?
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- Can you describe the video?
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- Tell me about what you see.
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- Tell me something interesting about what you see.
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- What's happening in the video?
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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=system_prompt,
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),
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)
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llm.register_function("get_weather", get_weather)
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llm.register_function("get_image", get_image)
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llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
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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("Let me check on that."))
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weather_function = FunctionSchema(
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name="get_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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get_image_function = FunctionSchema(
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name="get_image",
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description="Called when the user requests a description of their camera feed",
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properties={
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"user_id": {
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"type": "string",
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"description": "The ID of the user to grab the image from",
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},
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"question": {
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"type": "string",
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"description": "The question that the user is asking about the image",
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},
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},
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required=["user_id", "question"],
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)
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tools = ToolsSchema(standard_tools=[weather_function, get_image_function, restaurant_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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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: {client}")
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await maybe_capture_participant_camera(transport, client)
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client_id = get_transport_client_id(transport, client)
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# Kick off the conversation.
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context.add_message(
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{
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"role": "developer",
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"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
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}
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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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