225 lines
7.8 KiB
Python
225 lines
7.8 KiB
Python
#
|
|
# Copyright (c) 2024-2026, Daily
|
|
#
|
|
# SPDX-License-Identifier: BSD 2-Clause License
|
|
#
|
|
|
|
|
|
import asyncio
|
|
import os
|
|
import random
|
|
from datetime import datetime
|
|
|
|
from dotenv import load_dotenv
|
|
from loguru import logger
|
|
|
|
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
|
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
|
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
|
from pipecat.frames.frames import LLMRunFrame
|
|
from pipecat.pipeline.pipeline import Pipeline
|
|
from pipecat.pipeline.runner import PipelineRunner
|
|
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
|
from pipecat.processors.aggregators.llm_context import LLMContext
|
|
from pipecat.processors.aggregators.llm_response_universal import (
|
|
AssistantTurnStoppedMessage,
|
|
LLMContextAggregatorPair,
|
|
UserTurnStoppedMessage,
|
|
)
|
|
from pipecat.runner.types import RunnerArguments
|
|
from pipecat.runner.utils import create_transport
|
|
from pipecat.services.aws.nova_sonic.llm import AWSNovaSonicLLMService
|
|
from pipecat.services.llm_service import FunctionCallParams
|
|
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
|
from pipecat.transports.daily.transport import DailyParams
|
|
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
|
|
|
# Load environment variables
|
|
load_dotenv(override=True)
|
|
|
|
|
|
async def fetch_weather_from_api(params: FunctionCallParams):
|
|
temperature = (
|
|
random.randint(60, 85)
|
|
if params.arguments["format"] == "fahrenheit"
|
|
else random.randint(15, 30)
|
|
)
|
|
# Simulate a long network delay.
|
|
# You can continue chatting while waiting for this to complete.
|
|
# With Nova 2 Sonic (the default model), the assistant will respond
|
|
# appropriately once the function call is complete.
|
|
await asyncio.sleep(5)
|
|
await params.result_callback(
|
|
{
|
|
"conditions": "nice",
|
|
"temperature": temperature,
|
|
"location": params.arguments["location"],
|
|
"format": params.arguments["format"],
|
|
"timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
|
|
}
|
|
)
|
|
|
|
|
|
weather_function = FunctionSchema(
|
|
name="get_current_weather",
|
|
description="Get the current weather",
|
|
properties={
|
|
"location": {
|
|
"type": "string",
|
|
"description": "The city and state, e.g. San Francisco, CA",
|
|
},
|
|
"format": {
|
|
"type": "string",
|
|
"enum": ["celsius", "fahrenheit"],
|
|
"description": "The temperature unit to use. Infer this from the users location.",
|
|
},
|
|
},
|
|
required=["location", "format"],
|
|
)
|
|
|
|
# Create tools schema
|
|
tools = ToolsSchema(standard_tools=[weather_function])
|
|
|
|
|
|
# We use lambdas to defer transport parameter creation until the transport
|
|
# type is selected at runtime.
|
|
transport_params = {
|
|
"daily": lambda: DailyParams(
|
|
audio_in_enabled=True,
|
|
audio_out_enabled=True,
|
|
vad_analyzer=SileroVADAnalyzer(),
|
|
),
|
|
"twilio": lambda: FastAPIWebsocketParams(
|
|
audio_in_enabled=True,
|
|
audio_out_enabled=True,
|
|
vad_analyzer=SileroVADAnalyzer(),
|
|
),
|
|
"webrtc": lambda: TransportParams(
|
|
audio_in_enabled=True,
|
|
audio_out_enabled=True,
|
|
vad_analyzer=SileroVADAnalyzer(),
|
|
),
|
|
}
|
|
|
|
|
|
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
|
logger.info(f"Starting bot")
|
|
|
|
# Specify initial system instruction.
|
|
system_instruction = (
|
|
"You are a friendly assistant. The user and you will engage in a spoken dialog exchanging "
|
|
"the transcripts of a natural real-time conversation. Keep your responses short, generally "
|
|
"two or three sentences for chatty scenarios."
|
|
# HACK: if using the older Nova Sonic (pre-2) model, note that you need to inject a special
|
|
# bit of text into this instruction to allow the first assistant response to be
|
|
# programmatically triggered (which happens in the on_client_connected handler)
|
|
# f"{AWSNovaSonicLLMService.AWAIT_TRIGGER_ASSISTANT_RESPONSE_INSTRUCTION}"
|
|
)
|
|
|
|
# Create the AWS Nova Sonic LLM service
|
|
llm = AWSNovaSonicLLMService(
|
|
secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
|
|
access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
|
|
# as of 2025-12-09, these are the supported regions:
|
|
# - Nova 2 Sonic (the default model):
|
|
# - us-east-1
|
|
# - us-west-2
|
|
# - ap-northeast-1
|
|
# - Nova Sonic (the older model):
|
|
# - us-east-1
|
|
# - ap-northeast-1
|
|
region=os.getenv("AWS_REGION"),
|
|
session_token=os.getenv("AWS_SESSION_TOKEN"),
|
|
voice_id="tiffany",
|
|
# you could choose to pass instruction here rather than via context
|
|
# system_instruction=system_instruction
|
|
# you could choose to pass tools here rather than via context
|
|
# tools=tools
|
|
)
|
|
|
|
# Register function for function calls
|
|
# you can either register a single function for all function calls, or specific functions
|
|
# llm.register_function(None, fetch_weather_from_api)
|
|
llm.register_function(
|
|
"get_current_weather", fetch_weather_from_api, cancel_on_interruption=False
|
|
)
|
|
|
|
# Set up context and context management.
|
|
context = LLMContext(
|
|
messages=[
|
|
{"role": "system", "content": f"{system_instruction}"},
|
|
{
|
|
"role": "user",
|
|
"content": "Tell me a fun fact!",
|
|
},
|
|
],
|
|
tools=tools,
|
|
)
|
|
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(context)
|
|
|
|
# Build the pipeline
|
|
pipeline = Pipeline(
|
|
[
|
|
transport.input(),
|
|
user_aggregator,
|
|
llm,
|
|
transport.output(),
|
|
assistant_aggregator,
|
|
]
|
|
)
|
|
|
|
# Configure the pipeline task
|
|
task = PipelineTask(
|
|
pipeline,
|
|
params=PipelineParams(
|
|
enable_metrics=True,
|
|
enable_usage_metrics=True,
|
|
),
|
|
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
|
)
|
|
|
|
# Handle client connection event
|
|
@transport.event_handler("on_client_connected")
|
|
async def on_client_connected(transport, client):
|
|
logger.info(f"Client connected")
|
|
# Kick off the conversation.
|
|
await task.queue_frames([LLMRunFrame()])
|
|
# HACK: if using the older Nova Sonic (pre-2) model, you need this special way of
|
|
# triggering the first assistant response. Note that this trigger requires a special
|
|
# corresponding bit of text in the system instruction.
|
|
# await llm.trigger_assistant_response()
|
|
|
|
# Handle client disconnection events
|
|
@transport.event_handler("on_client_disconnected")
|
|
async def on_client_disconnected(transport, client):
|
|
logger.info(f"Client disconnected")
|
|
await task.cancel()
|
|
|
|
@user_aggregator.event_handler("on_user_turn_stopped")
|
|
async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
|
|
timestamp = f"[{message.timestamp}] " if message.timestamp else ""
|
|
line = f"{timestamp}user: {message.content}"
|
|
logger.info(f"Transcript: {line}")
|
|
|
|
@assistant_aggregator.event_handler("on_assistant_turn_stopped")
|
|
async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage):
|
|
timestamp = f"[{message.timestamp}] " if message.timestamp else ""
|
|
line = f"{timestamp}assistant: {message.content}"
|
|
logger.info(f"Transcript: {line}")
|
|
|
|
# Run the pipeline
|
|
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
|
await runner.run(task)
|
|
|
|
|
|
async def bot(runner_args: RunnerArguments):
|
|
"""Main bot entry point compatible with Pipecat Cloud."""
|
|
transport = await create_transport(runner_args, transport_params)
|
|
await run_bot(transport, runner_args)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from pipecat.runner.run import main
|
|
|
|
main()
|