Some improvements and cleanups in the SmallWebRTCTransport text examples.
This commit is contained in:
Filipi Fuchter
@@ -5,30 +5,18 @@
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#
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import argparse
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import asyncio
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import io
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import os
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import re
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import shutil
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import sys
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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 PIL import Image
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import (
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Frame,
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FunctionCallResultFrame,
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LLMMessagesAppendFrame,
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URLImageRawFrame,
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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.frame_processor import FrameDirection, FrameProcessor
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from pipecat.processors.frameworks.rtvi import (
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ActionResult,
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RTVIAction,
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@@ -38,6 +26,7 @@ from pipecat.processors.frameworks.rtvi import (
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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 TransportParams
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from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
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@@ -45,11 +34,43 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
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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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async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
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logger.info(f"Starting bot")
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@@ -58,111 +79,76 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
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params=TransportParams(),
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)
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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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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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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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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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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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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 = (
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arguments["run_immediately"] if "run_immediately" in arguments else True
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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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if run_immediately:
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await rtvi.interrupt_bot()
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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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# 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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@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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if run_immediately:
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frame = context_aggregator.user().get_context_frame()
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await rtvi.push_frame(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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return True
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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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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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runner = PipelineRunner(handle_sigint=False)
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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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await runner.run(task)
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if __name__ == "__main__":
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@@ -5,30 +5,19 @@
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#
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import argparse
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import asyncio
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import io
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import os
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import re
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import shutil
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import sys
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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 PIL import Image
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import (
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Frame,
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FunctionCallResultFrame,
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LLMMessagesAppendFrame,
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URLImageRawFrame,
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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.frame_processor import FrameDirection, FrameProcessor
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from pipecat.processors.frameworks.rtvi import (
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ActionResult,
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RTVIAction,
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@@ -40,6 +29,7 @@ from pipecat.processors.frameworks.rtvi import (
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)
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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 import OpenAIContextAggregatorPair
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.base_transport import TransportParams
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from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
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@@ -52,6 +42,41 @@ load_dotenv(override=True)
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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 = 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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async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
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logger.info(f"Starting bot")
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@@ -64,118 +89,83 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
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),
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)
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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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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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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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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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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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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 = 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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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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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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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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|
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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 = (
|
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arguments["run_immediately"] if "run_immediately" in arguments else True
|
||||
)
|
||||
@rtvi.event_handler("on_client_ready")
|
||||
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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if run_immediately:
|
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await rtvi.interrupt_bot()
|
||||
# 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 = {
|
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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)
|
||||
await task.queue_frames([rtvi_frame])
|
||||
|
||||
# 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)
|
||||
@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([context_aggregator.user().get_context_frame()])
|
||||
|
||||
if run_immediately:
|
||||
frame = context_aggregator.user().get_context_frame()
|
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await rtvi.push_frame(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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|
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return True
|
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@transport.event_handler("on_client_closed")
|
||||
async def on_client_closed(transport, client):
|
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logger.info(f"Client closed connection")
|
||||
await task.cancel()
|
||||
|
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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,
|
||||
)
|
||||
runner = PipelineRunner(handle_sigint=False)
|
||||
|
||||
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(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
),
|
||||
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([context_aggregator.user().get_context_frame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
|
||||
@transport.event_handler("on_client_closed")
|
||||
async def on_client_closed(transport, client):
|
||||
logger.info(f"Client closed connection")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False)
|
||||
|
||||
await runner.run(task)
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
Reference in New Issue
Block a user