afa880f523
Register the worker with PipelineRunner.add_workers() before calling run() instead. The worker argument still works but now emits a DeprecationWarning and will be removed in a future release. Update the runner docstrings, the run_test() helper, and all examples (including the asyncio.gather() forms) to use the new pattern.
147 lines
5.0 KiB
Python
147 lines
5.0 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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import shutil
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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 pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMRunFrame
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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.worker import PipelineParams, PipelineWorker
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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.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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# 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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),
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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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),
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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.environ["DEEPGRAM_API_KEY"])
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tts = CartesiaTTSService(
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api_key=os.environ["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 memory tools that let you store and recall information.
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Offer to remember things for the user, like their name, preferences, or anything they'd like.
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You can also recall things you've previously stored.
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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.environ["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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# https://github.com/modelcontextprotocol/servers/tree/main/src/memory
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async with MCPClient(
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server_params=StdioServerParameters(
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command=shutil.which("npx"),
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args=["-y", "@modelcontextprotocol/server-memory"],
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# env={"MEMORY_FILE_PATH": "/tmp/pipecat_memory.jsonl"}, # Optional: specify MEMORY_FILE_PATH
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),
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) as mcp:
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tools = await mcp.register_tools(llm)
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context = LLMContext(
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messages=[{"role": "user", "content": "Please introduce yourself."}],
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tools=tools,
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)
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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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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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worker = PipelineWorker(
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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 worker.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 worker.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.add_workers(worker)
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await runner.run()
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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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