66ad29b2b1
This lets us get handle_sigint from RunnerArguments which knows where the application is running and if SIGINT/SIGTERM should be handled or not.
142 lines
5.0 KiB
Python
142 lines
5.0 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 os
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from dotenv import load_dotenv
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from loguru import logger
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from mcp.client.session_group import StreamableHttpParameters
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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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.openai_llm_context import OpenAILLMContext
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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.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.mcp_service import MCPClient
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
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from pipecat.transports.services.daily import DailyParams
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load_dotenv(override=True)
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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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vad_analyzer=SileroVADAnalyzer(),
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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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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vad_analyzer=SileroVADAnalyzer(),
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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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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash")
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try:
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# Github MCP docs: https://github.com/github/github-mcp-server
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# Enable Github Copilot on your GitHub account. Free tier is ok. (https://github.com/settings/copilot)
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# Generate a personal access token. It must be a Fine-grained token, classic tokens are not supported. (https://github.com/settings/personal-access-tokens)
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# Set permissions you want to use (eg. "all repositories", "profile: read/write", etc)
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mcp = MCPClient(
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server_params=StreamableHttpParameters(
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url="https://api.githubcopilot.com/mcp/",
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headers={"Authorization": f"Bearer {os.getenv('GITHUB_PERSONAL_ACCESS_TOKEN')}"},
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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 mcp")
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logger.exception("error trace:")
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tools = await mcp.register_tools(llm)
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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 answer questions about the user's GitHub repositories and account.
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You have access to a number of tools provided by Github. Use any and all tools to help users.
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Your output will be converted to audio so don't include special characters in your answers.
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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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context = OpenAILLMContext(messages, tools)
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context_aggregator = llm.create_context_aggregator(context)
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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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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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)
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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([context_aggregator.user().get_context_frame()])
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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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