163 lines
5.5 KiB
Python
163 lines
5.5 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 argparse
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import os
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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 pipecat.frames.frames import (
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LLMMessagesAppendFrame,
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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.openai_llm_context import OpenAILLMContext
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from pipecat.processors.frameworks.rtvi import (
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ActionResult,
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RTVIAction,
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RTVIActionArgument,
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RTVIConfig,
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RTVIObserver,
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RTVIProcessor,
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RTVIServerMessageFrame,
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)
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from pipecat.services.openai import OpenAIContextAggregatorPair
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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.services.daily import DailyParams
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load_dotenv(override=True)
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# This is an example of a text-only chatbot using small webrtc tranport.
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# It uses the small webrtc transport prebuilt web UI.
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# https://github.com/pipecat-ai/small-webrtc-prebuilt
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def create_action_llm_append_to_messages(context_aggregator: OpenAIContextAggregatorPair):
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async def action_llm_append_to_messages_handler(
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rtvi: RTVIProcessor, service: str, arguments: dict[str, any]
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) -> ActionResult:
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run_immediately = arguments["run_immediately"] if "run_immediately" in arguments else True
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logger.info(f"run_immediately: {run_immediately}")
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if run_immediately:
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await rtvi.interrupt_bot()
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# We just interrupted the bot so it should be fine to use the
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# context directly instead of through frame.
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if "messages" in arguments and arguments["messages"]:
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frame = LLMMessagesAppendFrame(messages=arguments["messages"])
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await rtvi.push_frame(frame)
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frame = context_aggregator.user().get_context_frame()
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await rtvi.push_frame(frame)
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return True
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action_llm_append_to_messages = RTVIAction(
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service="llm",
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action="append_to_messages",
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result="bool",
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arguments=[
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RTVIActionArgument(name="messages", type="array"),
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RTVIActionArgument(name="run_immediately", type="bool"),
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],
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handler=action_llm_append_to_messages_handler,
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)
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return action_llm_append_to_messages
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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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"webrtc": lambda: TransportParams(),
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}
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async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
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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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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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messages = [
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{
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"role": "system",
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"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Respond to what the user said in a creative and helpful way.",
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},
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]
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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action_llm_append_to_messages = create_action_llm_append_to_messages(context_aggregator)
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rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
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rtvi.register_action(action_llm_append_to_messages)
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pipeline = Pipeline(
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[
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transport.input(),
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rtvi,
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context_aggregator.user(),
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llm,
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transport.output(),
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context_aggregator.assistant(),
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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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allow_interruptions=True,
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enable_metrics=True,
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),
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observers=[RTVIObserver(rtvi)],
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)
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@rtvi.event_handler("on_client_ready")
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async def on_client_ready(rtvi):
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logger.info("Pipecat client ready.")
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await rtvi.set_bot_ready()
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# This block is frontend UI specific
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# These messages are intended for small webrtc UI to only handle text
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# https://github.com/pipecat-ai/small-webrtc-prebuilt
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messages = {
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"show_text_container": True,
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"show_video_container": False,
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"show_debug_container": False,
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}
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rtvi_frame = RTVIServerMessageFrame(data=messages)
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await task.queue_frames([rtvi_frame])
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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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@transport.event_handler("on_client_closed")
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async def on_client_closed(transport, client):
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logger.info(f"Client closed connection")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=False)
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await runner.run(task)
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if __name__ == "__main__":
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from pipecat.examples.run import main
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main(run_example, transport_params=transport_params)
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