Updating foundation examples to use SmallWebRTCTransport and pipecat-ai-small-webrtc-prebuilt (#1534)
Co-authored-by: Filipi Fuchter <filipi@daily.co>
This commit is contained in:
Mark Backman
@@ -6,14 +6,11 @@
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import asyncio
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import os
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import sys
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import time
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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 openai.types.chat import ChatCompletionToolParam
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from runner import configure
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import (
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@@ -50,13 +47,12 @@ from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.sync.base_notifier import BaseNotifier
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from pipecat.sync.event_notifier import EventNotifier
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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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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from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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classifier_statement = """CRITICAL INSTRUCTION:
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You are a BINARY CLASSIFIER that must ONLY output "YES" or "NO".
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@@ -408,195 +404,203 @@ async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context
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await result_callback({"conditions": "nice", "temperature": "75"})
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, _) = await configure(session)
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async def run_bot(webrtc_connection: SmallWebRTCConnection):
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logger.info(f"Starting bot")
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transport = DailyTransport(
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room_url,
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None,
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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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vad_audio_passthrough=True,
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),
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)
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transport = SmallWebRTCTransport(
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webrtc_connection=webrtc_connection,
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params=TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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vad_audio_passthrough=True,
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),
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)
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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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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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# This is the LLM that will be used to detect if the user has finished a
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# statement. This doesn't really need to be an LLM, we could use NLP
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# libraries for that, but we have the machinery to use an LLM, so we might as well!
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statement_llm = AnthropicLLMService(
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api_key=os.getenv("ANTHROPIC_API_KEY"),
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model="claude-3-5-sonnet-20241022",
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)
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# This is the LLM that will be used to detect if the user has finished a
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# statement. This doesn't really need to be an LLM, we could use NLP
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# libraries for that, but we have the machinery to use an LLM, so we might as well!
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statement_llm = AnthropicLLMService(
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api_key=os.getenv("ANTHROPIC_API_KEY"),
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model="claude-3-5-sonnet-20241022",
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)
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# This is the regular LLM.
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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model="gpt-4o",
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)
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# Register a function_name of None to get all functions
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# sent to the same callback with an additional function_name parameter.
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llm.register_function("get_current_weather", fetch_weather_from_api)
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# This is the regular LLM.
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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model="gpt-4o",
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)
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# Register a function_name of None to get all functions
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# sent to the same callback with an additional function_name parameter.
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llm.register_function("get_current_weather", fetch_weather_from_api)
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tools = [
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ChatCompletionToolParam(
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type="function",
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function={
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"name": "get_current_weather",
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"description": "Get the current weather",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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tools = [
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ChatCompletionToolParam(
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type="function",
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function={
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"name": "get_current_weather",
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"description": "Get the current weather",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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"required": ["location", "format"],
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},
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"required": ["location", "format"],
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},
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)
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]
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messages = [
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{
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"role": "system",
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"content": conversational_system_message,
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},
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)
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]
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messages = [
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{
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"role": "system",
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"content": conversational_system_message,
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},
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]
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context = OpenAILLMContext(messages, tools)
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context_aggregator = llm.create_context_aggregator(context)
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# We have instructed the LLM to return 'YES' if it thinks the user
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# completed a sentence. So, if it's 'YES' we will return true in this
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# predicate which will wake up the notifier.
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async def wake_check_filter(frame):
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return frame.text == "YES"
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# This is a notifier that we use to synchronize the two LLMs.
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notifier = EventNotifier()
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# This turns the LLM context into an inference request to classify the user's speech
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# as complete or incomplete.
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statement_judge_context_filter = StatementJudgeContextFilter(notifier=notifier)
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# This sends a UserStoppedSpeakingFrame and triggers the notifier event
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completeness_check = CompletenessCheck(notifier=notifier)
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# # Notify if the user hasn't said anything.
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async def user_idle_notifier(frame):
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await notifier.notify()
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# Sometimes the LLM will fail detecting if a user has completed a
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# sentence, this will wake up the notifier if that happens.
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user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
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# We start with the gate open because we send an initial context frame
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# to start the conversation.
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bot_output_gate = OutputGate(notifier=notifier, start_open=True)
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async def block_user_stopped_speaking(frame):
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return not isinstance(frame, UserStoppedSpeakingFrame)
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async def pass_only_llm_trigger_frames(frame):
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return (
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isinstance(frame, OpenAILLMContextFrame)
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or isinstance(frame, LLMMessagesFrame)
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or isinstance(frame, StartInterruptionFrame)
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or isinstance(frame, StopInterruptionFrame)
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or isinstance(frame, FunctionCallInProgressFrame)
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or isinstance(frame, FunctionCallResultFrame)
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)
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pipeline = Pipeline(
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[
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transport.input(),
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stt,
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context_aggregator.user(),
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ParallelPipeline(
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[
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# Pass everything except UserStoppedSpeaking to the elements after
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# this ParallelPipeline
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FunctionFilter(filter=block_user_stopped_speaking),
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],
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[
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# Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
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# LLMMessagesFrame to the statement classifier LLM. The only frame this
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# sub-pipeline will output is a UserStoppedSpeakingFrame.
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statement_judge_context_filter,
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statement_llm,
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completeness_check,
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],
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[
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# Block everything except OpenAILLMContextFrame and LLMMessagesFrame
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FunctionFilter(filter=pass_only_llm_trigger_frames),
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llm,
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bot_output_gate, # Buffer all llm/tts output until notified.
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],
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),
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tts,
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user_idle,
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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, tools)
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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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enable_usage_metrics=True,
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),
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)
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# We have instructed the LLM to return 'YES' if it thinks the user
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# completed a sentence. So, if it's 'YES' we will return true in this
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# predicate which will wake up the notifier.
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async def wake_check_filter(frame):
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return frame.text == "YES"
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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")
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# Kick off the conversation.
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messages.append(
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{
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"role": "user",
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"content": "Start by just saying \"Hello I'm ready.\" Don't say anything else.",
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}
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)
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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# This is a notifier that we use to synchronize the two LLMs.
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notifier = EventNotifier()
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@transport.event_handler("on_app_message")
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async def on_app_message(transport, message):
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logger.debug(f"Received app message: {message}")
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if "message" not in message:
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return
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# This turns the LLM context into an inference request to classify the user's speech
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# as complete or incomplete.
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statement_judge_context_filter = StatementJudgeContextFilter(notifier=notifier)
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# This sends a UserStoppedSpeakingFrame and triggers the notifier event
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completeness_check = CompletenessCheck(notifier=notifier)
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# # Notify if the user hasn't said anything.
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async def user_idle_notifier(frame):
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await notifier.notify()
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# Sometimes the LLM will fail detecting if a user has completed a
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# sentence, this will wake up the notifier if that happens.
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user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
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# We start with the gate open because we send an initial context frame
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# to start the conversation.
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bot_output_gate = OutputGate(notifier=notifier, start_open=True)
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async def block_user_stopped_speaking(frame):
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return not isinstance(frame, UserStoppedSpeakingFrame)
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async def pass_only_llm_trigger_frames(frame):
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return (
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isinstance(frame, OpenAILLMContextFrame)
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or isinstance(frame, LLMMessagesFrame)
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or isinstance(frame, StartInterruptionFrame)
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or isinstance(frame, StopInterruptionFrame)
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or isinstance(frame, FunctionCallInProgressFrame)
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or isinstance(frame, FunctionCallResultFrame)
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)
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pipeline = Pipeline(
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await task.queue_frames(
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[
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transport.input(),
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stt,
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context_aggregator.user(),
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ParallelPipeline(
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[
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# Pass everything except UserStoppedSpeaking to the elements after
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# this ParallelPipeline
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FunctionFilter(filter=block_user_stopped_speaking),
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],
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[
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# Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
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# LLMMessagesFrame to the statement classifier LLM. The only frame this
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# sub-pipeline will output is a UserStoppedSpeakingFrame.
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statement_judge_context_filter,
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statement_llm,
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completeness_check,
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],
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[
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# Block everything except OpenAILLMContextFrame and LLMMessagesFrame
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FunctionFilter(filter=pass_only_llm_trigger_frames),
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llm,
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bot_output_gate, # Buffer all llm/tts output until notified.
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],
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),
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tts,
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user_idle,
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transport.output(),
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context_aggregator.assistant(),
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UserStartedSpeakingFrame(),
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TranscriptionFrame(user_id="", timestamp=time.time(), text=message["message"]),
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UserStoppedSpeakingFrame(),
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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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enable_usage_metrics=True,
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),
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)
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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_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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await transport.capture_participant_transcription(participant["id"])
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# Kick off the conversation.
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messages.append(
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{
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"role": "user",
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"content": "Start by just saying \"Hello I'm ready.\" Don't say anything else.",
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}
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)
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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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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@transport.event_handler("on_app_message")
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async def on_app_message(transport, message, sender):
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logger.debug(f"Received app message: {message} - {sender}")
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if "message" not in message:
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return
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runner = PipelineRunner(handle_sigint=False)
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await task.queue_frames(
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[
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UserStartedSpeakingFrame(),
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TranscriptionFrame(
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user_id=sender, timestamp=time.time(), text=message["message"]
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),
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UserStoppedSpeakingFrame(),
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]
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)
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runner = PipelineRunner()
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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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asyncio.run(main())
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from run import main
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main()
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