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
@@ -36,14 +36,11 @@ Requirements:
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The bot runs as part of a pipeline that processes audio frames and manages the conversation flow.
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"""
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import asyncio
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import os
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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 runner import configure
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from openai import audio
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.pipeline.pipeline import Pipeline
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@@ -51,13 +48,13 @@ 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.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
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from pipecat.services.mem0.memory import Mem0MemoryService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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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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@@ -115,7 +112,7 @@ async def get_initial_greeting(
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return "Hello! How can I help you today?"
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async def main():
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async def run_bot(webrtc_connection: SmallWebRTCConnection):
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"""Main bot execution function.
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Sets up and runs the bot pipeline including:
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@@ -127,116 +124,121 @@ async def main():
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"""
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# Note: You can pass the user_id as a parameter in API call
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USER_ID = "pipecat-demo-user"
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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# Set up Daily transport with video/audio parameters
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transport = DailyTransport(
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room_url,
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token,
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"Chatbot",
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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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transcription_enabled=True,
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),
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)
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logger.info(f"Starting bot")
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# Initialize text-to-speech service
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tts = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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voice_id="pNInz6obpgDQGcFmaJgB",
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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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# Initialize Mem0 memory service
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memory = Mem0MemoryService(
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api_key=os.getenv("MEM0_API_KEY"),
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user_id=USER_ID, # Unique identifier for the user
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# agent_id="agent1", # Optional identifier for the agent
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# run_id="session1", # Optional identifier for the run
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params=Mem0MemoryService.InputParams(
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search_limit=10,
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search_threshold=0.3,
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api_version="v2",
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system_prompt="Based on previous conversations, I recall: \n\n",
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add_as_system_message=True,
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position=1,
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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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# Initialize LLM service
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
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# Initialize text-to-speech service
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tts = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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voice_id="pNInz6obpgDQGcFmaJgB",
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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 personal assistant. You can remember things about the person you are talking to.
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Some Guidelines:
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- Make sure your responses are friendly yet short and concise.
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- If the user asks you to remember something, make sure to remember it.
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- Greet the user by their name if you know about it.
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""",
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},
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# Initialize Mem0 memory service
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memory = Mem0MemoryService(
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api_key=os.getenv("MEM0_API_KEY"),
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user_id=USER_ID, # Unique identifier for the user
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# agent_id="agent1", # Optional identifier for the agent
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# run_id="session1", # Optional identifier for the run
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params=Mem0MemoryService.InputParams(
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search_limit=10,
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search_threshold=0.3,
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api_version="v2",
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system_prompt="Based on previous conversations, I recall: \n\n",
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add_as_system_message=True,
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position=1,
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),
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)
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# Initialize LLM service
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
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messages = [
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{
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"role": "system",
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"content": """You are a personal assistant. You can remember things about the person you are talking to.
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Some Guidelines:
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- Make sure your responses are friendly yet short and concise.
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- If the user asks you to remember something, make sure to remember it.
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- Greet the user by their name if you know about it.
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""",
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},
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]
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# Set up conversation context and management
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# The context_aggregator will automatically collect conversation context
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
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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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memory,
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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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# Set up conversation context and management
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# The context_aggregator will automatically collect conversation context
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
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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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observers=[RTVIObserver(rtvi)],
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)
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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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memory,
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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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@rtvi.event_handler("on_client_ready")
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async def on_client_ready(rtvi):
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await rtvi.set_bot_ready()
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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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# Get personalized greeting based on user memories. Can pass agent_id and run_id as per requirement of the application to manage short term memory or agent specific memory.
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greeting = await get_initial_greeting(
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memory_client=memory.memory_client, user_id=USER_ID, agent_id=None, run_id=None
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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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observers=[RTVIObserver(rtvi)],
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)
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# Add the greeting as an assistant message to start the conversation
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context.add_message({"role": "assistant", "content": greeting})
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@rtvi.event_handler("on_client_ready")
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async def on_client_ready(rtvi):
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await rtvi.set_bot_ready()
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# Queue the context frame to start the conversation
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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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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@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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# Get personalized greeting based on user memories. Can pass agent_id and run_id as per requirement of the application to manage short term memory or agent specific memory.
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greeting = await get_initial_greeting(
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memory_client=memory.memory_client, user_id=USER_ID, agent_id=None, run_id=None
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)
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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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# Add the greeting as an assistant message to start the conversation
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context.add_message({"role": "assistant", "content": greeting})
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# Queue the context frame to start the conversation
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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print(f"Participant left: {participant}")
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await task.cancel()
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runner = PipelineRunner()
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await runner.run(task)
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runner = PipelineRunner(handle_sigint=False)
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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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