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
250
examples/mcp/mcp-stdio.py
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250
examples/mcp/mcp-stdio.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 asyncio
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import io
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import json
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import os
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import re
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import shutil
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from mcp import StdioServerParameters
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from PIL import Image
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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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FunctionCallResultFrame,
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LLMRunFrame,
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URLImageRawFrame,
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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.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 create_transport
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from pipecat.services.anthropic.llm import AnthropicLLMService
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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.mcp_service import MCPClient
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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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class UrlToImageProcessor(FrameProcessor):
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def __init__(self, aiohttp_session: aiohttp.ClientSession, **kwargs):
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super().__init__(**kwargs)
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self._aiohttp_session = aiohttp_session
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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 isinstance(frame, FunctionCallResultFrame):
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await self.push_frame(frame, direction)
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image_url = self.extract_url(frame.result)
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if image_url:
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await self.run_image_process(image_url)
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# sometimes we get multiple image urls- process 1 at a time
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await asyncio.sleep(1)
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else:
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await self.push_frame(frame, direction)
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def extract_url(self, text: str):
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try:
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data = json.loads(text)
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if "artObject" in data:
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return data["artObject"]["webImage"]["url"]
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if "artworks" in data and len(data["artworks"]):
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return data["artworks"][0]["webImage"]["url"]
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except (json.JSONDecodeError, KeyError, TypeError):
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pass
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return None
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async def run_image_process(self, image_url: str):
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try:
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logger.debug(f"handling image from url: '{image_url}'")
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async with self._aiohttp_session.get(image_url) as response:
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image_stream = io.BytesIO(await response.content.read())
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image = Image.open(image_stream)
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image = image.convert("RGB")
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frame = URLImageRawFrame(
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url=image_url, image=image.tobytes(), size=image.size, format="RGB"
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)
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await self.push_frame(frame)
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except Exception as e:
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error_msg = f"Error handling image url {image_url}: {str(e)}"
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logger.error(error_msg)
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# full list of tools available from rijksmuseum MCP:
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# - get_artwork_details
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# - get_artwork_image
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# - get_user_sets
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# - get_user_set_details
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# - open_image_in_browser
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# - get_artist_timeline
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mcp_tools_filter = ["get_artwork_details", "get_artwork_image", "open_image_in_browser"]
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def open_image_output_filter(output: str):
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pattern = r"Successfully opened image in browser: "
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text_to_print = re.sub(pattern, "", output)
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print(f"🖼️ link to high resolution artwork: {text_to_print}")
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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_out_enabled=True,
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video_out_width=1024,
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video_out_height=1024,
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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_out_enabled=True,
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video_out_width=1024,
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video_out_height=1024,
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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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# Create an HTTP session for API calls
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async with aiohttp.ClientSession() as session:
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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 = f"""
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You are a helpful LLM in a voice call.
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Your goal is to demonstrate your capabilities in a succinct way.
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You have access to tools to search the Rijksmuseum collection.
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Offer, for example, to show a floral still life, use the `search_artwork` tool.
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The tool may respond with a JSON object with an `artworks` array. Choose the art from that array.
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Once the tool has responded, tell the user the title and use the `open_image_in_browser` tool.
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Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.
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Respond to what the user said in a creative and helpful way.
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Don't overexplain what you are doing.
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Just respond with short sentences when you are carrying out tool calls.
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"""
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llm = AnthropicLLMService(
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api_key=os.getenv("ANTHROPIC_API_KEY"),
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settings=AnthropicLLMService.Settings(
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system_instruction=system_prompt,
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),
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)
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try:
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mcp = MCPClient(
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server_params=StdioServerParameters(
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command=shutil.which("npx"),
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# https://github.com/r-huijts/rijksmuseum-mcp
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args=["-y", "mcp-server-rijksmuseum"],
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env={"RIJKSMUSEUM_API_KEY": os.getenv("RIJKSMUSEUM_API_KEY")},
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),
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# Optional
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tools_filter=mcp_tools_filter, # Optional
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tools_output_filters={"open_image_in_browser": open_image_output_filter},
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)
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except Exception as e:
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logger.error(f"error setting up mcp")
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logger.exception("error trace:")
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mcp_image = UrlToImageProcessor(aiohttp_session=session)
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tools = {}
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try:
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tools = await mcp.register_tools(llm)
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except Exception as e:
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logger.error(f"error registering tools")
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logger.exception("error trace:")
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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,
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user_aggregator, # User spoken responses
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llm, # LLM
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tts, # TTS
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mcp_image, # URL image -> output
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transport.output(), # Transport bot output
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assistant_aggregator, # Assistant spoken responses and tool context
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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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# 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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if not os.getenv("RIJKSMUSEUM_API_KEY"):
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logger.error(
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f"Please set RIJKSMUSEUM_API_KEY environment variable for this example. See https://github.com/r-huijts/rijksmuseum-mcp and https://www.rijksmuseum.nl/en/register?redirectUrl=https://www.https://www.rijksmuseum.nl/en/rijksstudio/my/profile"
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)
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import sys
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sys.exit(1)
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from pipecat.runner.run import main
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main()
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