From 89484e281d8a8500f280b00e2f19d0969cb7f10a Mon Sep 17 00:00:00 2001 From: Mark Backman Date: Mon, 12 Jan 2026 10:11:58 -0500 Subject: [PATCH] Remove foundational examples 41a and 41b --- examples/foundational/41a-text-only-webrtc.py | 164 ---------------- .../foundational/41b-text-and-audio-webrtc.py | 180 ------------------ 2 files changed, 344 deletions(-) delete mode 100644 examples/foundational/41a-text-only-webrtc.py delete mode 100644 examples/foundational/41b-text-and-audio-webrtc.py diff --git a/examples/foundational/41a-text-only-webrtc.py b/examples/foundational/41a-text-only-webrtc.py deleted file mode 100644 index 18ac367b5..000000000 --- a/examples/foundational/41a-text-only-webrtc.py +++ /dev/null @@ -1,164 +0,0 @@ -# -# Copyright (c) 2024-2026, Daily -# -# SPDX-License-Identifier: BSD 2-Clause License -# - -import os - -from dotenv import load_dotenv -from loguru import logger - -from pipecat.frames.frames import ( - LLMMessagesAppendFrame, - LLMRunFrame, -) -from pipecat.pipeline.pipeline import Pipeline -from pipecat.pipeline.runner import PipelineRunner -from pipecat.pipeline.task import PipelineParams, PipelineTask -from pipecat.processors.aggregators.llm_context import LLMContext -from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair -from pipecat.processors.frameworks.rtvi import ( - ActionResult, - RTVIAction, - RTVIActionArgument, - RTVIConfig, - RTVIObserver, - RTVIProcessor, - RTVIServerMessageFrame, -) -from pipecat.runner.types import RunnerArguments -from pipecat.runner.utils import create_transport -from pipecat.services.openai.llm import OpenAIContextAggregatorPair, OpenAILLMService -from pipecat.transports.base_transport import BaseTransport, TransportParams - -load_dotenv(override=True) - - -# This is an example of a text-only chatbot using small webrtc tranport. -# It uses the small webrtc transport prebuilt web UI. -# https://github.com/pipecat-ai/small-webrtc-prebuilt - - -def create_action_llm_append_to_messages(context_aggregator: OpenAIContextAggregatorPair): - async def action_llm_append_to_messages_handler( - rtvi: RTVIProcessor, service: str, arguments: dict[str, any] - ) -> ActionResult: - run_immediately = arguments["run_immediately"] if "run_immediately" in arguments else True - logger.info(f"run_immediately: {run_immediately}") - if run_immediately: - await rtvi.interrupt_bot() - # We just interrupted the bot so it should be fine to use the - # context directly instead of through frame. - if "messages" in arguments and arguments["messages"]: - frame = LLMMessagesAppendFrame(messages=arguments["messages"]) - await rtvi.push_frame(frame) - - frame = LLMRunFrame() - await rtvi.push_frame(frame) - return True - - action_llm_append_to_messages = RTVIAction( - service="llm", - action="append_to_messages", - result="bool", - arguments=[ - RTVIActionArgument(name="messages", type="array"), - RTVIActionArgument(name="run_immediately", type="bool"), - ], - handler=action_llm_append_to_messages_handler, - ) - return action_llm_append_to_messages - - -# We store functions so objects (e.g. SileroVADAnalyzer) don't get -# instantiated. The function will be called when the desired transport gets -# selected. -transport_params = { - "webrtc": lambda: TransportParams(), -} - - -async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): - logger.info(f"Starting bot") - - llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY")) - - messages = [ - { - "role": "system", - "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.", - }, - ] - - context = LLMContext(messages) - context_aggregator = LLMContextAggregatorPair(context) - - action_llm_append_to_messages = create_action_llm_append_to_messages(context_aggregator) - rtvi = RTVIProcessor(config=RTVIConfig(config=[])) - rtvi.register_action(action_llm_append_to_messages) - - pipeline = Pipeline( - [ - transport.input(), - rtvi, - context_aggregator.user(), - llm, - transport.output(), - context_aggregator.assistant(), - ] - ) - - task = PipelineTask( - pipeline, - params=PipelineParams( - enable_metrics=True, - enable_usage_metrics=True, - ), - idle_timeout_secs=runner_args.pipeline_idle_timeout_secs, - observers=[RTVIObserver(rtvi)], - ) - - @rtvi.event_handler("on_client_ready") - async def on_client_ready(rtvi): - logger.info("Pipecat client ready.") - await rtvi.set_bot_ready() - - # This block is frontend UI specific - # These messages are intended for small webrtc UI to only handle text - # https://github.com/pipecat-ai/small-webrtc-prebuilt - messages = { - "show_text_container": True, - "show_video_container": False, - "show_debug_container": False, - } - - rtvi_frame = RTVIServerMessageFrame(data=messages) - await task.queue_frames([rtvi_frame]) - - @transport.event_handler("on_client_connected") - async def on_client_connected(transport, client): - logger.info(f"Client connected: {client}") - # Kick off the conversation. - await task.queue_frames([LLMRunFrame()]) - - @transport.event_handler("on_client_disconnected") - async def on_client_disconnected(transport, client): - logger.info(f"Client disconnected") - await task.cancel() - - runner = PipelineRunner(handle_sigint=runner_args.handle_sigint) - - await runner.run(task) - - -async def bot(runner_args: RunnerArguments): - """Main bot entry point compatible with Pipecat Cloud.""" - transport = await create_transport(runner_args, transport_params) - await run_bot(transport, runner_args) - - -if __name__ == "__main__": - from pipecat.runner.run import main - - main() diff --git a/examples/foundational/41b-text-and-audio-webrtc.py b/examples/foundational/41b-text-and-audio-webrtc.py deleted file mode 100644 index d98bfdedd..000000000 --- a/examples/foundational/41b-text-and-audio-webrtc.py +++ /dev/null @@ -1,180 +0,0 @@ -# -# Copyright (c) 2024-2026, Daily -# -# SPDX-License-Identifier: BSD 2-Clause License -# - -import os - -from dotenv import load_dotenv -from loguru import logger - -from pipecat.audio.vad.silero import SileroVADAnalyzer -from pipecat.frames.frames import ( - LLMMessagesAppendFrame, - LLMRunFrame, -) -from pipecat.pipeline.pipeline import Pipeline -from pipecat.pipeline.runner import PipelineRunner -from pipecat.pipeline.task import PipelineParams, PipelineTask -from pipecat.processors.aggregators.llm_context import LLMContext -from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair -from pipecat.processors.frameworks.rtvi import ( - ActionResult, - RTVIAction, - RTVIActionArgument, - RTVIConfig, - RTVIObserver, - RTVIProcessor, - RTVIServerMessageFrame, -) -from pipecat.runner.types import RunnerArguments -from pipecat.runner.utils import create_transport -from pipecat.services.cartesia.tts import CartesiaTTSService -from pipecat.services.deepgram.stt import DeepgramSTTService -from pipecat.services.openai.llm import OpenAIContextAggregatorPair, OpenAILLMService -from pipecat.transports.base_transport import BaseTransport, TransportParams - -load_dotenv(override=True) - -# This is an example of a chatbot in which a user can speak and/or type text to communicate with the bot. -# It uses the small webrtc transport prebuilt web UI. -# https://github.com/pipecat-ai/small-webrtc-prebuilt - - -def create_action_llm_append_to_messages(context_aggregator: OpenAIContextAggregatorPair): - async def action_llm_append_to_messages_handler( - rtvi: RTVIProcessor, service: str, arguments: dict[str, any] - ) -> ActionResult: - run_immediately = arguments["run_immediately"] if "run_immediately" in arguments else True - - if run_immediately: - await rtvi.interrupt_bot() - - # We just interrupted the bot so it should be fine to use the - # context directly instead of through frame. - if "messages" in arguments and arguments["messages"]: - mess = arguments["messages"] - frame = LLMMessagesAppendFrame(messages=arguments["messages"]) - await rtvi.push_frame(frame) - - if run_immediately: - frame = LLMRunFrame() - await rtvi.push_frame(frame) - - return True - - action_llm_append_to_messages = RTVIAction( - service="llm", - action="append_to_messages", - result="bool", - arguments=[ - RTVIActionArgument(name="messages", type="array"), - RTVIActionArgument(name="run_immediately", type="bool"), - ], - handler=action_llm_append_to_messages_handler, - ) - return action_llm_append_to_messages - - -# We store functions so objects (e.g. SileroVADAnalyzer) don't get -# instantiated. The function will be called when the desired transport gets -# selected. -transport_params = { - "webrtc": lambda: TransportParams( - audio_in_enabled=True, - audio_out_enabled=True, - vad_analyzer=SileroVADAnalyzer(), - ), -} - - -async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): - logger.info(f"Starting bot") - - stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY")) - - llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY")) - - tts = CartesiaTTSService( - api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121" - ) - - messages = [ - { - "role": "system", - "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.", - }, - ] - - context = LLMContext(messages) - context_aggregator = LLMContextAggregatorPair(context) - - action_llm_append_to_messages = create_action_llm_append_to_messages(context_aggregator) - rtvi = RTVIProcessor(config=RTVIConfig(config=[])) - rtvi.register_action(action_llm_append_to_messages) - - pipeline = Pipeline( - [ - transport.input(), - rtvi, - stt, - context_aggregator.user(), - llm, - tts, - transport.output(), - context_aggregator.assistant(), - ] - ) - - task = PipelineTask( - pipeline, - params=PipelineParams( - enable_metrics=True, - enable_usage_metrics=True, - ), - idle_timeout_secs=runner_args.pipeline_idle_timeout_secs, - observers=[RTVIObserver(rtvi)], - ) - - @rtvi.event_handler("on_client_ready") - async def on_client_ready(rtvi): - logger.info("Pipecat client ready.") - await rtvi.set_bot_ready() - - # This block is frontend UI specific - # These messages are intended for small webrtc UI to only handle text - # https://github.com/pipecat-ai/small-webrtc-prebuilt - messages = { - "show_text_container": True, - "show_debug_container": False, - } - rtvi_frame = RTVIServerMessageFrame(data=messages) - await task.queue_frames([rtvi_frame]) - - @transport.event_handler("on_client_connected") - async def on_client_connected(transport, client): - logger.info(f"Client connected: {client}") - # Kick off the conversation. - await task.queue_frames([LLMRunFrame()]) - - @transport.event_handler("on_client_disconnected") - async def on_client_disconnected(transport, client): - logger.info(f"Client disconnected") - await task.cancel() - - runner = PipelineRunner(handle_sigint=runner_args.handle_sigint) - - await runner.run(task) - - -async def bot(runner_args: RunnerArguments): - """Main bot entry point compatible with Pipecat Cloud.""" - transport = await create_transport(runner_args, transport_params) - await run_bot(transport, runner_args) - - -if __name__ == "__main__": - from pipecat.runner.run import main - - main()