240 lines
9.1 KiB
Python
240 lines
9.1 KiB
Python
#
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# Copyright (c) 2024–2025, 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 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 mcp.client.session_group import SseServerParameters
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from PIL import Image
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
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from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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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 LLMContextAggregatorPair
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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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pattern = r"!\[[^\]]*\]\((https?://[^)]+\.(png|jpg|jpeg|PNG|JPG|JPEG|gif))\)"
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match = re.search(pattern, text)
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if match:
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return match.group(1)
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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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# We store functions so objects (e.g. SileroVADAnalyzer) don't get
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# instantiated. The function will be called when the desired transport gets
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# selected.
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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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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
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turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
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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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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
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turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
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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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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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llm = AnthropicLLMService(
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api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-7-sonnet-latest"
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)
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system = f"""
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You are a helpful LLM in a WebRTC 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 the earliest Rembrandt work from the museum. 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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messages = [{"role": "system", "content": system}]
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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-error setting up mcp"],
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env={"RIJKSMUSEUM_API_KEY": os.getenv("RIJKSMUSEUM_API_KEY")},
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)
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)
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except Exception as e:
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logger.error(f"error setting up rijksmuseum mcp")
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logger.exception("error trace:")
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try:
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# https://docs.mcp.run/integrating/tutorials/mcp-run-sse-openai-agents/
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# ie. "https://www.mcp.run/api/mcp/sse?..."
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# ensure the profile has a tool or few installed
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mcp_run = MCPClient(server_params=SseServerParameters(url=os.getenv("MCP_RUN_SSE_URL")))
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except Exception as e:
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logger.error(f"error setting up mcp.run")
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logger.exception("error trace:")
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tools = {}
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run_tools = {}
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try:
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tools = await mcp.register_tools(llm)
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run_tools = await mcp_run.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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all_standard_tools = run_tools.standard_tools + tools.standard_tools
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all_tools = ToolsSchema(standard_tools=all_standard_tools)
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context = LLMContext(messages, all_tools)
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context_aggregator = LLMContextAggregatorPair(context)
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mcp_image_processor = UrlToImageProcessor(aiohttp_session=session)
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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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context_aggregator.user(), # User spoken responses
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llm, # LLM
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tts, # TTS
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mcp_image_processor, # URL image -> output
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transport.output(), # Transport bot output
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context_aggregator.assistant(), # 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") or not os.getenv("MCP_RUN_SSE_URL"):
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logger.error(
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f"Please set RIJKSMUSEUM_API_KEY and MCP_RUN_SSE_URL environment variables. See https://github.com/r-huijts/rijksmuseum-mcp and https://mcp.run"
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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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