chore: import upstream snapshot with attribution
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import boto3
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from semantic_kernel.agents import BedrockAgent, BedrockAgentThread
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"""
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The following sample demonstrates how to use an already existing
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Bedrock Agent within Semantic Kernel. This sample requires that you
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have an existing agent created either previously in code or via the
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AWS Console.
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This sample uses the following main component(s):
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- a Bedrock agent
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You will learn how to retrieve a Bedrock agent and talk to it.
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"""
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# Replace "your-agent-id" with the ID of the agent you want to use
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AGENT_ID = "your-agent-id"
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async def main():
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client = boto3.client("bedrock-agent")
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agent_model = client.get_agent(agentId=AGENT_ID)["agent"]
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bedrock_agent = BedrockAgent(agent_model)
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thread: BedrockAgentThread = None
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try:
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while True:
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user_input = input("User:> ")
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if user_input == "exit":
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print("\n\nExiting chat...")
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break
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# Invoke the agent
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# The chat history is maintained in the session
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async for response in bedrock_agent.invoke(
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messages=user_input,
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thread=thread,
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):
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print(f"Bedrock agent: {response}")
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thread = response.thread
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except KeyboardInterrupt:
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print("\n\nExiting chat...")
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return False
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except EOFError:
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print("\n\nExiting chat...")
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return False
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finally:
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# Cleanup: Delete the thread
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await thread.delete() if thread else None
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# Sample output (using anthropic.claude-3-haiku-20240307-v1:0):
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# User:> Hi, my name is John.
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# Bedrock agent: Hello John. How can I help you?
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# User:> What is my name?
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# Bedrock agent: Your name is John.
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if __name__ == "__main__":
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asyncio.run(main())
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