update SmallWebRTCTransport text examples with new run_example
This commit is contained in:
vipyne
@@ -7,6 +7,7 @@
|
|||||||
import argparse
|
import argparse
|
||||||
import os
|
import os
|
||||||
|
|
||||||
|
import aiohttp
|
||||||
from dotenv import load_dotenv
|
from dotenv import load_dotenv
|
||||||
from loguru import logger
|
from loguru import logger
|
||||||
|
|
||||||
@@ -28,9 +29,8 @@ from pipecat.processors.frameworks.rtvi import (
|
|||||||
)
|
)
|
||||||
from pipecat.services.openai import OpenAIContextAggregatorPair
|
from pipecat.services.openai import OpenAIContextAggregatorPair
|
||||||
from pipecat.services.openai.llm import OpenAILLMService
|
from pipecat.services.openai.llm import OpenAILLMService
|
||||||
from pipecat.transports.base_transport import TransportParams
|
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||||
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
|
from pipecat.transports.services.daily import DailyParams
|
||||||
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
|
||||||
|
|
||||||
load_dotenv(override=True)
|
load_dotenv(override=True)
|
||||||
|
|
||||||
@@ -71,87 +71,92 @@ def create_action_llm_append_to_messages(context_aggregator: OpenAIContextAggreg
|
|||||||
return action_llm_append_to_messages
|
return action_llm_append_to_messages
|
||||||
|
|
||||||
|
|
||||||
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
|
# 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_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
|
||||||
logger.info(f"Starting bot")
|
logger.info(f"Starting bot")
|
||||||
|
|
||||||
transport = SmallWebRTCTransport(
|
# Create an HTTP session for API calls
|
||||||
webrtc_connection=webrtc_connection,
|
async with aiohttp.ClientSession() as session:
|
||||||
params=TransportParams(),
|
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||||
)
|
|
||||||
|
|
||||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
messages = [
|
||||||
|
{
|
||||||
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.",
|
||||||
"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 = OpenAILLMContext(messages)
|
|
||||||
context_aggregator = llm.create_context_aggregator(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(
|
context = OpenAILLMContext(messages)
|
||||||
pipeline,
|
context_aggregator = llm.create_context_aggregator(context)
|
||||||
params=PipelineParams(
|
|
||||||
allow_interruptions=True,
|
|
||||||
enable_metrics=True,
|
|
||||||
),
|
|
||||||
observers=[RTVIObserver(rtvi)],
|
|
||||||
)
|
|
||||||
|
|
||||||
@rtvi.event_handler("on_client_ready")
|
action_llm_append_to_messages = create_action_llm_append_to_messages(context_aggregator)
|
||||||
async def on_client_ready(rtvi):
|
rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
|
||||||
logger.info("Pipecat client ready.")
|
rtvi.register_action(action_llm_append_to_messages)
|
||||||
await rtvi.set_bot_ready()
|
|
||||||
|
|
||||||
# This block is frontend UI specific
|
pipeline = Pipeline(
|
||||||
# These messages are intended for small webrtc UI to only handle text
|
[
|
||||||
# https://github.com/pipecat-ai/small-webrtc-prebuilt
|
transport.input(),
|
||||||
messages = {
|
rtvi,
|
||||||
"show_text_container": True,
|
context_aggregator.user(),
|
||||||
"show_video_container": False,
|
llm,
|
||||||
"show_debug_container": False,
|
transport.output(),
|
||||||
}
|
context_aggregator.assistant(),
|
||||||
rtvi_frame = RTVIServerMessageFrame(data=messages)
|
]
|
||||||
await task.queue_frames([rtvi_frame])
|
)
|
||||||
|
|
||||||
@transport.event_handler("on_client_connected")
|
task = PipelineTask(
|
||||||
async def on_client_connected(transport, client):
|
pipeline,
|
||||||
logger.info(f"Client connected: {client}")
|
params=PipelineParams(
|
||||||
# Kick off the conversation.
|
allow_interruptions=True,
|
||||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
enable_metrics=True,
|
||||||
|
),
|
||||||
|
observers=[RTVIObserver(rtvi)],
|
||||||
|
)
|
||||||
|
|
||||||
@transport.event_handler("on_client_disconnected")
|
@rtvi.event_handler("on_client_ready")
|
||||||
async def on_client_disconnected(transport, client):
|
async def on_client_ready(rtvi):
|
||||||
logger.info(f"Client disconnected")
|
logger.info("Pipecat client ready.")
|
||||||
|
await rtvi.set_bot_ready()
|
||||||
|
|
||||||
@transport.event_handler("on_client_closed")
|
# This block is frontend UI specific
|
||||||
async def on_client_closed(transport, client):
|
# These messages are intended for small webrtc UI to only handle text
|
||||||
logger.info(f"Client closed connection")
|
# https://github.com/pipecat-ai/small-webrtc-prebuilt
|
||||||
await task.cancel()
|
messages = {
|
||||||
|
"show_text_container": True,
|
||||||
|
"show_video_container": False,
|
||||||
|
"show_debug_container": False,
|
||||||
|
}
|
||||||
|
rtvi_frame = RTVIServerMessageFrame(data=messages)
|
||||||
|
await task.queue_frames([rtvi_frame])
|
||||||
|
|
||||||
runner = PipelineRunner(handle_sigint=False)
|
@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()])
|
||||||
|
|
||||||
await runner.run(task)
|
@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)
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
from run import main
|
from run import main
|
||||||
|
|
||||||
main()
|
main(run_example, transport_params=transport_params)
|
||||||
|
|||||||
@@ -7,6 +7,7 @@
|
|||||||
import argparse
|
import argparse
|
||||||
import os
|
import os
|
||||||
|
|
||||||
|
import aiohttp
|
||||||
from dotenv import load_dotenv
|
from dotenv import load_dotenv
|
||||||
from loguru import logger
|
from loguru import logger
|
||||||
|
|
||||||
@@ -31,9 +32,8 @@ from pipecat.services.cartesia.tts import CartesiaTTSService
|
|||||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||||
from pipecat.services.openai import OpenAIContextAggregatorPair
|
from pipecat.services.openai import OpenAIContextAggregatorPair
|
||||||
from pipecat.services.openai.llm import OpenAILLMService
|
from pipecat.services.openai.llm import OpenAILLMService
|
||||||
from pipecat.transports.base_transport import TransportParams
|
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||||
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
|
from pipecat.transports.services.daily import DailyParams
|
||||||
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
|
||||||
|
|
||||||
load_dotenv(override=True)
|
load_dotenv(override=True)
|
||||||
|
|
||||||
@@ -77,98 +77,103 @@ def create_action_llm_append_to_messages(context_aggregator: OpenAIContextAggreg
|
|||||||
return action_llm_append_to_messages
|
return action_llm_append_to_messages
|
||||||
|
|
||||||
|
|
||||||
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
|
# 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_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
|
||||||
logger.info(f"Starting bot")
|
logger.info(f"Starting bot")
|
||||||
|
|
||||||
transport = SmallWebRTCTransport(
|
# Create an HTTP session for API calls
|
||||||
webrtc_connection=webrtc_connection,
|
async with aiohttp.ClientSession() as session:
|
||||||
params=TransportParams(
|
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||||
audio_in_enabled=True,
|
|
||||||
audio_out_enabled=True,
|
|
||||||
vad_analyzer=SileroVADAnalyzer(),
|
|
||||||
),
|
|
||||||
)
|
|
||||||
|
|
||||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
llm = OpenAILLMService(api_key=os.getenv("OPENAI_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"
|
||||||
|
)
|
||||||
|
|
||||||
tts = CartesiaTTSService(
|
messages = [
|
||||||
api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
|
{
|
||||||
)
|
"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.",
|
||||||
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 = OpenAILLMContext(messages)
|
|
||||||
context_aggregator = llm.create_context_aggregator(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(
|
context = OpenAILLMContext(messages)
|
||||||
pipeline,
|
context_aggregator = llm.create_context_aggregator(context)
|
||||||
params=PipelineParams(
|
|
||||||
allow_interruptions=True,
|
|
||||||
enable_metrics=True,
|
|
||||||
),
|
|
||||||
observers=[RTVIObserver(rtvi)],
|
|
||||||
)
|
|
||||||
|
|
||||||
@rtvi.event_handler("on_client_ready")
|
action_llm_append_to_messages = create_action_llm_append_to_messages(context_aggregator)
|
||||||
async def on_client_ready(rtvi):
|
rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
|
||||||
logger.info("Pipecat client ready.")
|
rtvi.register_action(action_llm_append_to_messages)
|
||||||
await rtvi.set_bot_ready()
|
|
||||||
|
|
||||||
# This block is frontend UI specific
|
pipeline = Pipeline(
|
||||||
# These messages are intended for small webrtc UI to only handle text
|
[
|
||||||
# https://github.com/pipecat-ai/small-webrtc-prebuilt
|
transport.input(),
|
||||||
messages = {
|
rtvi,
|
||||||
"show_text_container": True,
|
stt,
|
||||||
"show_debug_container": False,
|
context_aggregator.user(),
|
||||||
}
|
llm,
|
||||||
rtvi_frame = RTVIServerMessageFrame(data=messages)
|
tts,
|
||||||
await task.queue_frames([rtvi_frame])
|
transport.output(),
|
||||||
|
context_aggregator.assistant(),
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|
||||||
@transport.event_handler("on_client_connected")
|
task = PipelineTask(
|
||||||
async def on_client_connected(transport, client):
|
pipeline,
|
||||||
logger.info(f"Client connected: {client}")
|
params=PipelineParams(
|
||||||
# Kick off the conversation.
|
allow_interruptions=True,
|
||||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
enable_metrics=True,
|
||||||
|
),
|
||||||
|
observers=[RTVIObserver(rtvi)],
|
||||||
|
)
|
||||||
|
|
||||||
@transport.event_handler("on_client_disconnected")
|
@rtvi.event_handler("on_client_ready")
|
||||||
async def on_client_disconnected(transport, client):
|
async def on_client_ready(rtvi):
|
||||||
logger.info(f"Client disconnected")
|
logger.info("Pipecat client ready.")
|
||||||
|
await rtvi.set_bot_ready()
|
||||||
|
|
||||||
@transport.event_handler("on_client_closed")
|
# This block is frontend UI specific
|
||||||
async def on_client_closed(transport, client):
|
# These messages are intended for small webrtc UI to only handle text
|
||||||
logger.info(f"Client closed connection")
|
# https://github.com/pipecat-ai/small-webrtc-prebuilt
|
||||||
await task.cancel()
|
messages = {
|
||||||
|
"show_text_container": True,
|
||||||
|
"show_debug_container": False,
|
||||||
|
}
|
||||||
|
rtvi_frame = RTVIServerMessageFrame(data=messages)
|
||||||
|
await task.queue_frames([rtvi_frame])
|
||||||
|
|
||||||
runner = PipelineRunner(handle_sigint=False)
|
@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()])
|
||||||
|
|
||||||
await runner.run(task)
|
@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)
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
from run import main
|
from run import main
|
||||||
|
|
||||||
main()
|
main(run_example, transport_params=transport_params)
|
||||||
|
|||||||
Reference in New Issue
Block a user