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.
231 lines
8.2 KiB
Python
231 lines
8.2 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 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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Frame,
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LLMFullResponseEndFrame,
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LLMFullResponseStartFrame,
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LLMRunFrame,
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TextFrame,
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TTSSpeakFrame,
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UserImageRequestFrame,
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)
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from pipecat.pipeline.parallel_pipeline import ParallelPipeline
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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 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, FrameProcessor
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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.llm_service import FunctionCallParams
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from pipecat.services.moondream.vision import MoondreamService
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from pipecat.services.openai.llm import OpenAILLMService
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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 fetch_user_image(params: FunctionCallParams):
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"""Fetch the user image.
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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. The result_callback will be invoked once the
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image is 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. In this case, we don't want the requested
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# image to be added to the context because we will process it with
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# Moondream. 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=False,
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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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class MoondreamTextFrameWrapper(FrameProcessor):
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"""Wraps Moondream-provided TextFrames with LLM response start/end frames.
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This processor detects TextFrames and automatically wraps them with
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LLMFullResponseStartFrame and LLMFullResponseEndFrame to provide proper
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response boundaries for downstream processors.
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"""
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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# If we receive a TextFrame, wrap it with response start/end frames
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if isinstance(frame, TextFrame):
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await self.push_frame(LLMFullResponseStartFrame(), direction)
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await self.push_frame(frame, direction)
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await self.push_frame(LLMFullResponseEndFrame(), direction)
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else:
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# For all other frames, just pass them through
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await self.push_frame(frame, direction)
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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 = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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settings=OpenAILLMService.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. You are able to describe images from the user camera.",
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),
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)
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llm.register_function("fetch_user_image", fetch_user_image)
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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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fetch_image_function = FunctionSchema(
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name="fetch_user_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=[fetch_image_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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# If you run into weird description, try with use_cpu=True
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moondream = MoondreamService()
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# Wrap TextFrames with LLM response start/end frames, which makes Moondream
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# output be treated like LLM responses for the purpose of context
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# aggregation. Without this, the assistant context aggregator would ignore
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# Moondream output (if the TTS service is disabled).
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moondream_text_wrapper = MoondreamTextFrameWrapper()
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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, # User responses
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ParallelPipeline(
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[llm], # LLM
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[moondream, moondream_text_wrapper],
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),
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tts, # TTS
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transport.output(), # Transport bot output
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assistant_aggregator, # Assistant spoken responses
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]
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)
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task = PipelineTask(
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pipeline,
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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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# Set the participant ID in the image requester
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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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