Initial StrandsAgentsProcessor implementation

examples/foundational/07m-interruptible-aws-strands.py

@@ -0,0 +1,169 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+from dotenv import load_dotenv
+from loguru import logger
+
+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.openai_llm_context import (
+    OpenAILLMContext,
+    LLMAssistantContextAggregator,
+    LLMUserContextAggregator,
+)
+from pipecat.processors.frameworks.strands_agents import StrandsAgentsProcessor
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.aws.stt import AWSTranscribeSTTService
+from pipecat.services.aws.tts import AWSPollyTTSService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+# Strands agent setup
+try:
+    from strands import Agent, tool
+    from strands.models import BedrockModel
+except ImportError:
+    logger.warning("Strands not installed. Please install with: pip install strands-agents")
+    Agent = None
+    BedrockModel = None
+
+load_dotenv(override=True)
+
+# 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(),
+    ),
+}
+
+def build_agent(model_id: str, max_tokens: int):
+    """Create and configure a Strands agent for NAB customer service coaching.
+    
+    Args:
+        model_id: The AWS Bedrock model ID to use
+        max_tokens: Maximum tokens for the model
+        
+    Returns:
+        Configured Strands Agent
+    """
+    @tool
+    def check_weather(location: str) -> str:
+        if location.lower() == "san francisco":
+            return "The weather in San Francisco is sunny and 30 degrees."
+        elif location.lower() == "sydney":
+            return "The weather in Sydney is cloudy and 20 degrees."
+        else:
+            return "I'm not sure about the weather in that location."
+    
+    agent = Agent(
+        model=BedrockModel(
+            model_id=model_id,
+            max_tokens=max_tokens,
+        ),
+        tools=[check_weather],
+        system_prompt="You are a helpful assistant that can check the weather in a given location."
+    )
+
+    return agent
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    stt = AWSTranscribeSTTService()
+
+    tts = AWSPollyTTSService(
+        region="us-west-2",  # only specific regions support generative TTS
+        voice_id="Joanna",
+        params=AWSPollyTTSService.InputParams(engine="generative", rate="1.1"),
+    )
+
+    # Create Strands agent processor
+    try:
+        agent = build_agent(
+            model_id="us.anthropic.claude-3-5-haiku-20241022-v1:0", 
+            max_tokens=8000
+        )
+        llm = StrandsAgentsProcessor(agent=agent)
+        logger.info("Successfully created Strands agent for NAB customer service coaching")
+    except Exception as e:
+        logger.error(f"Failed to create Strands agent: {e}")
+        raise ValueError(
+            "Unable to create Strands processor. Please ensure you have properly "
+            "installed strands-agents and configured your AWS credentials."
+        )
+
+    # Setup context aggregators for message handling
+    context = OpenAILLMContext()
+    tma_in = LLMUserContextAggregator(context=context)
+    tma_out = LLMAssistantContextAggregator(context=context)
+
+    pipeline = Pipeline([
+        transport.input(),      # Transport user input
+        stt,                   # Speech-to-text
+        tma_in,               # User context aggregator
+        llm,                  # Strands Agents processor
+        tts,                  # Text-to-speech
+        transport.output(),   # Transport bot output
+        tma_out              # Assistant context aggregator
+    ])
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "user", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([LLMRunFrame()])
+
+    @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=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()