MongoDB com agente Bedrock: tutorial rápido

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{
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  "fields": [
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    {
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      "type": "vector",
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      "path": "embedding",
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      "numDimensions": 1024,
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      "similarity": "cosine"
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    },
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    {
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      "type" : "filter",
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      "path" : "metadata"
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    },
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    {
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      "type" : "filter",
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      "path" : "text"
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    },
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  ]
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}

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!pip install boto3

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import boto3
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from botocore.exceptions import ClientError
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import os
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import random
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import getpass
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# Get AWS credentials from user
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aws_access_key = getpass.getpass("Enter your AWS Access Key: ")
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aws_secret_key = getpass.getpass("Enter your AWS Secret Key: ")

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bedrock_agent_runtime = boto3.client('bedrock-agent-runtime',
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                                    aws_access_key_id=aws_access_key,
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                                    aws_secret_access_key=aws_secret_key,
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                                    region_name="us-east-1")
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# Define agent IDs (replace these with your actual agent IDs)
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agent_id = getpass.getpass("Enter your agent ID")
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agent_alias_id = getpass.getpass("Enter your agent Alias ID")

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def randomise_session_id():
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   """
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   Generate a random session ID.
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   Returns:
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       str: A random session ID.
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   """
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   return str(random.randint(1000, 9999))
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def data_stream_generator(response):
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   """
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   Generator to yield data chunks from the response.
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   Args:
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       response (dict): The response dictionary.
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   Yields:
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       str: The next chunk of data.
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   """
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   for event in response["completion"]:
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       chunk = event.get("chunk", {})
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       if "bytes" in chunk:
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           yield chunk["bytes"].decode()
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def invoke_agent(bedrock_agent_runtime, agent_id, agent_alias_id, session_id, prompt):
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   """
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   Sends a prompt for the agent to process and respond to, streaming the response data.
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   Args:
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       bedrock_agent_runtime (boto3 client): The runtime client to invoke the agent.
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       agent_id (str): The unique identifier of the agent to use.
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       agent_alias_id (str): The alias of the agent to use.
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       session_id (str): The unique identifier of the session. Use the same value across requests to continue the same conversation.
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       prompt (str): The prompt that you want the agent to complete.
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   Returns:
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       str: The response from the agent.
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   """
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   try:
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       response = bedrock_agent_runtime.invoke_agent(
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           agentId=agent_id,
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           agentAliasId=agent_alias_id,
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           sessionId=session_id,
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           inputText=prompt,
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       )
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       # Use the data stream generator to stream the response
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       ret_response = ''.join(data_stream_generator(response))
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       return ret_response
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   except Exception as e:
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       return f"Error invoking agent: {e}"

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# Initialize chat history and session ID
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session_id = randomise_session_id()
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while True:
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   prompt = input("Enter your prompt (or type 'exit' to quit): ")
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   if prompt.lower() == 'exit':
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       break
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   response = invoke_agent(bedrock_agent_runtime, agent_id, agent_alias_id, session_id, prompt)
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   print("Agent Response:")
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   print(response)