pipecat/examples/aws-strands/black-box.py

#
# Copyright (c) 2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

import argparse
import asyncio
import os

from dotenv import load_dotenv
from loguru import logger
from strands import Agent, tool
from strands.models import BedrockModel

from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.llm_service import FunctionCallParams
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
from pipecat.transports.services.daily import DailyParams

load_dotenv(override=True)

"""This example demonstrates how to use the Strands agent with Pipecat.

You can delegate complex, multi-step tasks to the Strands agent, which can cycle through LLM-based reasoning and tool calls to accomplish the task.

Try asking: "What's the weather where the Golden Gate Bridge is?"
"""

# Strands agent tools


@tool
def get_location_name_from_landmark(landmark: str) -> str:
    """
    Get the location name from a landmark.

    Args:
        landmark (str): The name of the landmark, e.g. "Golden Gate Bridge".
    """
    # Simulate fetching location
    return "San Francisco, CA"


@tool
def get_lat_long_from_location_name(location: str) -> dict:
    """
    Get the latitude and longitude for a location name.

    Args:
        location (str): The city and state, e.g. "San Francisco, CA".
    """
    # Simulate fetching lat/long from a geocoding service
    return {"lat": 37.7749, "long": -122.4194}


@tool
def get_current_weather_from_lat_long(lat: float, long: float) -> dict:
    """
    Get the current weather for a specific latitude and longitude.

    Args:
        lat (float): The latitude of the location.
        long (float): The longitude of the location.
    """
    # Simulate fetching weather data from a weather service
    return {"conditions": "nice", "temperature": "75"}


# 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 = {
    "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_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
    logger.info(f"Starting bot")

    strands_agent = Agent(
        model=BedrockModel(
            model_id="us.anthropic.claude-3-7-sonnet-20250219-v1:0", max_tokens=64000
        ),
        tools=[
            get_location_name_from_landmark,
            get_lat_long_from_location_name,
            get_current_weather_from_lat_long,
        ],
    )

    async def handle_location_or_weather_related_queries(params: FunctionCallParams, query: str):
        """
        Handle location or weather related queries.

        Args:
            query (str): The user's query, e.g. "What's the weather where the Golden Gate Bridge is?".
        """
        # Run in a background thread
        # (Otherwise the agent blocks the event loop; one effect of that is that we don't hear
        # "let me check on that" until the agent finishes)
        loop = asyncio.get_running_loop()
        result = await loop.run_in_executor(None, strands_agent, query)
        await params.result_callback(result.message)

    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))

    tts = CartesiaTTSService(
        api_key=os.getenv("CARTESIA_API_KEY"),
        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
    )

    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))

    llm.register_direct_function(handle_location_or_weather_related_queries)

    @llm.event_handler("on_function_calls_started")
    async def on_function_calls_started(service, function_calls):
        await tts.queue_frame(TTSSpeakFrame("Let me check on that."))

    tools = ToolsSchema(standard_tools=[handle_location_or_weather_related_queries])

    messages = [
        {
            "role": "system",
            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
        },
    ]

    context = OpenAILLMContext(messages, tools)
    context_aggregator = llm.create_context_aggregator(context)

    pipeline = Pipeline(
        [
            transport.input(),
            stt,
            context_aggregator.user(),
            llm,
            tts,
            transport.output(),
            context_aggregator.assistant(),
        ]
    )

    task = PipelineTask(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
    )

    @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([context_aggregator.user().get_context_frame()])

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(transport, client):
        logger.info(f"Client disconnected")
        await task.cancel()

    runner = PipelineRunner(handle_sigint=handle_sigint)

    await runner.run(task)


if __name__ == "__main__":
    from pipecat.examples.run import main

    main(run_example, transport_params=transport_params)