Remove foundational examples 41a and 41b
This commit is contained in:
Mark Backman
@@ -1,164 +0,0 @@
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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 os
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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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LLMRunFrame,
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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.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.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.openai.llm import OpenAIContextAggregatorPair, OpenAILLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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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 = LLMRunFrame()
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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_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot")
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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 = LLMContext(messages)
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context_aggregator = LLMContextAggregatorPair(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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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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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([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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from pipecat.runner.run import main
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main()
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@@ -1,180 +0,0 @@
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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 os
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import (
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LLMMessagesAppendFrame,
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LLMRunFrame,
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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.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.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.openai.llm import OpenAIContextAggregatorPair, OpenAILLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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load_dotenv(override=True)
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# This is an example of a chatbot in which a user can speak and/or type text to communicate with the bot.
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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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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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mess = arguments["messages"]
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frame = LLMMessagesAppendFrame(messages=arguments["messages"])
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await rtvi.push_frame(frame)
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if run_immediately:
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frame = LLMRunFrame()
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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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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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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
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)
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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 says in a creative and helpful way. Explain to the User they can speak or type text to communicate with you.",
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},
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]
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context = LLMContext(messages)
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context_aggregator = LLMContextAggregatorPair(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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stt,
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context_aggregator.user(),
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llm,
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tts,
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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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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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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_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([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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from pipecat.runner.run import main
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main()
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Reference in New Issue
Block a user