d3021b4590
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.
332 lines
11 KiB
Python
332 lines
11 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 glob
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import json
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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 ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMRunFrame, 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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BASE_FILENAME = "/tmp/pipecat_conversation_"
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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 get_image(params: FunctionCallParams):
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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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async def get_saved_conversation_filenames(params: FunctionCallParams):
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# Construct the full pattern including the BASE_FILENAME
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full_pattern = f"{BASE_FILENAME}*.json"
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# Use glob to find all matching files
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matching_files = glob.glob(full_pattern)
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logger.debug(f"matching files: {matching_files}")
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await params.result_callback({"filenames": matching_files})
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async def save_conversation(params: FunctionCallParams):
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timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
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filename = f"{BASE_FILENAME}{timestamp}.json"
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logger.debug(
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f"writing conversation to {filename}\n{json.dumps(params.context.get_messages(), indent=4)}"
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)
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try:
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with open(filename, "w") as file:
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messages = params.context.get_messages()
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# remove the last message (the instruction to save the context)
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messages.pop()
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json.dump(messages, file, indent=2)
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await params.result_callback({"success": True})
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except Exception as e:
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logger.debug(f"error saving conversation: {e}")
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await params.result_callback({"success": False, "error": str(e)})
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async def load_conversation(params: FunctionCallParams):
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filename = params.arguments["filename"]
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logger.debug(f"loading conversation from {filename}")
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try:
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with open(filename, "r") as file:
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params.context.set_messages(json.load(file))
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await params.result_callback(
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{
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"success": True,
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"message": "The most recent conversation has been loaded. Awaiting further instructions.",
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}
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)
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except Exception as e:
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await params.result_callback({"success": False, "error": str(e)})
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system_instruction = """You are a helpful assistant in a voice conversation. Your goal is to demonstrate your
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capabilities in a succinct way. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Keep responses concise. Respond to what the user said in a creative
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can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative
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and helpful way.
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You have several tools you can use to help you.
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You can respond to questions about the weather using the get_weather tool.
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You can save the current conversation using the save_conversation tool. This tool allows you to save
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the current conversation to external storage. If the user asks you to save the conversation, use this
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save_conversation too.
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You can load a saved conversation using the load_conversation tool. This tool allows you to load a
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conversation from external storage. You can get a list of conversations that have been saved using the
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get_saved_conversation_filenames 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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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 users location.",
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},
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},
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required=["location", "format"],
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)
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save_conversation_function = FunctionSchema(
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name="save_conversation",
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description="Save the current conversation. Use this function to persist the current conversation to external storage.",
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properties={
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"user_request_text": {
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"type": "string",
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"description": "The text of the user's request to save the conversation.",
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}
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},
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required=["user_request_text"],
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)
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get_filenames_function = FunctionSchema(
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name="get_saved_conversation_filenames",
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description="Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
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properties={},
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required=[],
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)
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load_conversation_function = FunctionSchema(
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name="load_conversation",
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description="Load a conversation history. Use this function to load a conversation history into the current session.",
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properties={
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"filename": {
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"type": "string",
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"description": "The filename of the conversation history to load.",
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}
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},
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required=["filename"],
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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(
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standard_tools=[
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weather_function,
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save_conversation_function,
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get_filenames_function,
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load_conversation_function,
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get_image_function,
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]
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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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llm = GoogleLLMService(
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api_key=os.getenv("GOOGLE_API_KEY"),
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system_instruction=system_instruction,
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)
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# you can either register a single function for all function calls, or specific functions
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# llm.register_function(None, fetch_weather_from_api)
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llm.register_function("get_current_weather", fetch_weather_from_api)
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llm.register_function("save_conversation", save_conversation)
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llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
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llm.register_function("load_conversation", load_conversation)
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llm.register_function("get_image", get_image)
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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(), # Transport user input
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stt, # STT
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user_aggregator,
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llm, # LLM
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tts,
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transport.output(), # Transport bot 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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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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