Reorganize examples into topic-based subfolders
Move 304 examples from a flat numbered directory into 14 descriptive subfolders: getting-started, services (speech + function-calling), transcription, vision, realtime, persistent-context, context-summarization, update-settings (stt/tts/llm), turn-management, thinking-and-mcp, transports, video-avatar, video-processing, and features. Strip numbered prefixes from filenames (e.g. 07c-interruptible-deepgram.py becomes services/speech/deepgram.py) since the folder context makes them redundant. Keep numbered prefixes only in getting-started/ where ordering matters. Update eval script paths and README to match the new structure.
This commit is contained in:
Mark Backman
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examples/persistent-context/aws-nova-sonic.py
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295
examples/persistent-context/aws-nova-sonic.py
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#
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# Copyright (c) 2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import asyncio
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import glob
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import json
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import os
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from datetime import datetime
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMRunFrame
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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.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.aws.nova_sonic.llm import AWSNovaSonicLLMService
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from pipecat.services.llm_service import FunctionCallParams
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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BASE_FILENAME = "/tmp/pipecat_conversation_"
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async def fetch_weather_from_api(params: FunctionCallParams):
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temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
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await params.result_callback(
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{
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"conditions": "nice",
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"temperature": temperature,
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"format": params.arguments["format"],
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"timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
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}
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)
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async def get_saved_conversation_filenames(params: FunctionCallParams):
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# Construct the full pattern including the BASE_FILENAME
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full_pattern = f"{BASE_FILENAME}*.json"
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# Use glob to find all matching files
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matching_files = glob.glob(full_pattern)
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logger.debug(f"matching files: {matching_files}")
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await params.result_callback({"filenames": matching_files})
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# async def get_saved_conversation_filenames(
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# function_name, tool_call_id, args, llm, context, result_callback
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# ):
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# pattern = re.compile(re.escape(BASE_FILENAME) + "\\d{8}_\\d{6}\\.json$")
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# matching_files = []
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# for filename in os.listdir("."):
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# if pattern.match(filename):
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# matching_files.append(filename)
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# await result_callback({"filenames": matching_files})
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async def save_conversation(params: FunctionCallParams):
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timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
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filename = f"{BASE_FILENAME}{timestamp}.json"
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try:
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with open(filename, "w") as file:
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messages = params.context.get_messages()
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# remove the last few messages. in reverse order, they are:
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# - the in progress save tool call
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# - the invocation of the save tool call
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# - the user ask to save (which may encompass one or more messages)
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# the simplest thing to do is to pop messages until the last one is an assistant
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# response
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while messages and not (
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messages[-1].get("role") == "assistant" and "content" in messages[-1]
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):
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messages.pop()
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if messages: # we never expect this to be empty
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logger.debug(
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f"writing conversation to {filename}\n{json.dumps(messages, indent=4)}"
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)
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json.dump(messages, file, indent=2)
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await params.result_callback({"success": True})
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except Exception as e:
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await params.result_callback({"success": False, "error": str(e)})
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async def load_conversation(params: FunctionCallParams):
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async def _reset():
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filename = params.arguments["filename"]
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logger.debug(f"loading conversation from {filename}")
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try:
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with open(filename, "r") as file:
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messages = json.load(file)
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# HACK: if using the older Nova Sonic (pre-2) model, you need a special way of
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# triggering the first assistant response. The call to trigger_assistant_response(),
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# commented out below, is part of this.
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# messages.append(
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# {
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# "role": "developer",
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# "content": f"{AWSNovaSonicLLMService.AWAIT_TRIGGER_ASSISTANT_RESPONSE_INSTRUCTION}",
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# }
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# )
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# If the last message isn't from the user, add a message asking for a recap
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if messages and messages[-1].get("role") != "user":
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messages.append(
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{
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"role": "user",
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"content": "Can you catch me up on what we were talking about?",
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}
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)
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params.context.set_messages(messages)
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await params.llm.reset_conversation()
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# await params.llm.trigger_assistant_response()
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except Exception as e:
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await params.result_callback({"success": False, "error": str(e)})
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asyncio.create_task(_reset())
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get_current_weather_tool = FunctionSchema(
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name="get_current_weather",
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description="Get the current weather",
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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 user's location.",
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},
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},
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required=["location", "format"],
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)
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save_conversation_tool = FunctionSchema(
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name="save_conversation",
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description="Save the current conversation. Use this function to persist the current conversation to external storage.",
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properties={},
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required=[],
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)
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get_saved_conversation_filenames_tool = FunctionSchema(
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name="get_saved_conversation_filenames",
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description="Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
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properties={},
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required=[],
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)
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load_conversation_tool = FunctionSchema(
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name="load_conversation",
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description="Load a conversation history. Use this function to load a conversation history into the current session.",
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properties={
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"filename": {
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"type": "string",
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"description": "The filename of the conversation history to load.",
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}
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},
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required=["filename"],
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)
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tools = ToolsSchema(
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standard_tools=[
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get_current_weather_tool,
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save_conversation_tool,
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get_saved_conversation_filenames_tool,
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load_conversation_tool,
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]
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)
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# We use lambdas to defer transport parameter creation until the transport
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# type is selected at runtime.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot")
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# Specify initial system instruction.
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system_instruction = (
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"You are a friendly assistant. The user and you will engage in a spoken dialog exchanging "
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"the transcripts of a natural real-time conversation. Keep your responses short, generally "
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"two or three sentences for chatty scenarios. "
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# HACK: if using the older Nova Sonic (pre-2) model, note that you need to inject a special
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# bit of text into this instruction to allow the first assistant response to be
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# programmatically triggered (which happens in the on_client_connected handler)
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# f"{AWSNovaSonicLLMService.AWAIT_TRIGGER_ASSISTANT_RESPONSE_INSTRUCTION}"
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)
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llm = AWSNovaSonicLLMService(
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secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
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access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
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region=os.getenv("AWS_REGION"), # as of 2025-05-06, us-east-1 is the only supported region
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settings=AWSNovaSonicLLMService.Settings(
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voice="tiffany", # matthew, tiffany, amy
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system_instruction=system_instruction,
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),
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# you could choose to pass tools here rather than via context
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# tools=tools
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)
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llm.register_function("get_current_weather", fetch_weather_from_api)
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llm.register_function("save_conversation", save_conversation)
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llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
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llm.register_function("load_conversation", load_conversation)
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context = LLMContext(tools=tools)
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
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)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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user_aggregator,
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llm, # LLM
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transport.output(), # Transport bot output
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assistant_aggregator,
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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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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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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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context.add_message(
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{"role": "developer", "content": "Please introduce yourself to the user."}
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)
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await task.queue_frames([LLMRunFrame()])
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# HACK: if using the older Nova Sonic (pre-2) model, you need this special way of
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# triggering the first assistant response. Note that this trigger requires a special
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# corresponding bit of text in the system instruction.
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# await llm.trigger_assistant_response()
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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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await task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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