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Merge pull request #3412 from pipecat-ai/mb/remove-41a-b

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Remove foundational examples 41a and 41b
This commit is contained in:
Mark Backman
2026-01-13 16:45:26 -05:00
committed by GitHub
parent 5d90f4ea06 89484e281d
commit 87d0dc9e24
2 changed files with 0 additions and 344 deletions
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164
examples/foundational/41a-text-only-webrtc.py
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@@ -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()

180
examples/foundational/41b-text-and-audio-webrtc.py
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@@ -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()