AWS Strands demos

examples/aws-strands/black-box.py

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+#
+# 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)