ModelErrorException: An error occurred (ModelErrorException) when calling the InvokeModel operation: The system encountered an unexpected error during processing. Try your request again.

That probably issue could be due a temporary error, for that you can implement a retry mechanism in your code to handle the ModelErrorException. A library like backoff can be used to manage retries with exponential backoff, allowing the system to recover from temporary errors. In case of a ModelErrorException, you can catch and handle it gracefully by logging the error, skipping the affected document, or trying a different model if available.

Example of implementing retries with exponential backoff:

import backoff
import boto3
from botocore.exceptions import ClientError

@backoff.on_exception(backoff.expo, ClientError, max_tries=5)
def invoke_model(body, model_id):
    bedrock_client = boto3.client('bedrock')
    response = bedrock_client.invoke_model(
        body=body,
        contentType='application/json',
        accept='application/json',
        modelId=model_id
    )
    return response

Catch and handle errors:

from tqdm import tqdm
from botocore.exceptions import ClientError

bedrock_client = boto3.client('bedrock')
model_id = 'your_model_id_here'

for i, doc in tqdm(enumerate(df['text'])):
    query = "Please answer the following question based on the document"
    prompt_template = f"""
    <documents>
    <document index="1">
    {doc}
    </document_content>
    </documents>
    You are an AI assistant answering questions based on the document.
    Please answer in the format shown in the example.
    e.g., <sentence1> Example sentence </sentence1>
    {query}
    """

    body = json.dumps({
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 4096,
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": prompt_template
                    }
                ]
            }
        ]
    })

    try:
        response = invoke_model(body, model_id)
        response_body = json.loads(response.get('body').read())
        response_text = response_body['content'][0]['text']
        df1.at[i, 'sentence'] = response_text
    except ClientError as e:
        if e.response['Error']['Code'] == 'ModelErrorException':
            print(f"ModelErrorException occurred for document {i}. Skipping this document.")
            continue
        else:
            raise e

Hello,

The error ModelErrorException suggests that the issue may be related to the availability or the state of the model you're trying to use.

Few things you can try to troubleshoot the problem:

1. Check the model status: Ensure that the model you're trying to use (in this case, "sonnet") is in a healthy and available state.

2. Retry the request: As the error message suggests, try your request again. The issue may be temporary, and retrying the request may resolve the problem.

3. Check for resource limits: Ensure that you haven't reached any service limits or quotas that might be causing the issue.

4. Check for network issues: Intermittent errors can sometimes be caused by network connectivity issues. Ensure that your network connection is stable and reliable.

5. Add retry logic: Implement a retry mechanism in your code to handle intermittent errors. When such error occurs the logic will wait for 30-60 seconds and then retry.

6. Contact AWS Support: If the issue persists, you may want to consider contacting AWS Support to investigate the problem further.

[1] https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html