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
182
examples/services/speech/aws-strands.py
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182
examples/services/speech/aws-strands.py
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#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMMessagesAppendFrame
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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.processors.frameworks.strands_agents import StrandsAgentsProcessor
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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.stt import AWSTranscribeSTTService
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from pipecat.services.aws.tts import AWSPollyTTSService
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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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# Strands agent setup
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try:
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from strands import Agent, tool
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from strands.models import BedrockModel
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except ImportError:
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logger.warning("Strands not installed. Please install with: pip install strands-agents")
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Agent = None
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BedrockModel = None
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load_dotenv(override=True)
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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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def build_agent(model_id: str, max_tokens: int):
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"""Create and configure a Strands agent for NAB customer service coaching.
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Args:
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model_id: The AWS Bedrock model ID to use
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max_tokens: Maximum tokens for the model
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Returns:
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Configured Strands Agent
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"""
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@tool
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def check_weather(location: str) -> str:
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if location.lower() == "san francisco":
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return "The weather in San Francisco is sunny and 75 degrees."
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elif location.lower() == "sydney":
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return "The weather in Sydney is cloudy and 60 degrees."
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else:
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return "I'm not sure about the weather in that location."
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agent = Agent(
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model=BedrockModel(
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model_id=model_id,
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max_tokens=max_tokens,
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),
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tools=[check_weather],
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system_prompt="You are a helpful assistant that can check the weather in a given location.",
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)
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return agent
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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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stt = AWSTranscribeSTTService()
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tts = AWSPollyTTSService(
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region="us-west-2", # only specific regions support generative TTS
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settings=AWSPollyTTSService.Settings(
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voice="Joanna",
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engine="generative",
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rate="1.1",
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),
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)
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# Create Strands agent processor
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try:
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agent = build_agent(model_id="us.anthropic.claude-sonnet-4-6", max_tokens=8000)
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llm = StrandsAgentsProcessor(agent=agent)
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logger.info("Successfully created Strands agent for NAB customer service coaching")
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except Exception as e:
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logger.error(f"Failed to create Strands agent: {e}")
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raise ValueError(
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"Unable to create Strands processor. Please ensure you have properly "
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"installed strands-agents and configured your AWS credentials."
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)
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# Setup context aggregators for message handling
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context = LLMContext()
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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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stt, # Speech-to-text
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user_aggregator, # User responses
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llm, # Strands Agents processor
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tts, # Text-to-speech
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transport.output(), # Transport bot output
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assistant_aggregator, # Assistant spoken responses
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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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await task.queue_frames(
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[
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LLMMessagesAppendFrame(
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messages=[
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{
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"role": "developer",
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"content": f"Greet the user and introduce yourself. Don't use emojis.",
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}
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],
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run_llm=True,
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)
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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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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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