To use Amazon Bedrock in a workflow for processing documents, such as summarizing text from S3 and saving results, you can leverage AWS services like Lambda, S3 event triggers, and Bedrock’s API. First, set up an S3 bucket to store incoming documents and configure an event notification to trigger an AWS Lambda function when a new file is uploaded. The Lambda function will handle reading the document, invoking Bedrock’s summarization model, and storing the output. Ensure your AWS Identity and Access Management (IAM) roles grant permissions for Lambda to access S3 and Bedrock, and that Bedrock’s model access is enabled in the AWS console.
For example, when a PDF or text file is uploaded to S3, the Lambda function uses the AWS SDK (like Boto3 in Python) to retrieve the file. If the document is in a non-text format (e.g., scanned PDF), you might first use Amazon Textract to extract text. Once the text is ready, the Lambda function sends it to Bedrock using the InvokeModel API, specifying a summarization-focused model like Anthropic’s Claude. You’ll need to structure the input prompt for the model, such as “Summarize the following document in three sentences: [text]”. Bedrock returns the summarized text, which the Lambda function then writes to a designated output S3 bucket or path.
To ensure reliability, add error handling for scenarios like large documents exceeding model token limits or transient API failures. For large texts, split the content into chunks and process them iteratively. Use Amazon CloudWatch to log errors and monitor invocation metrics. For cost optimization, consider asynchronous processing with Step Functions for complex workflows or batch operations. Finally, encrypt input/output files using S3 server-side encryption and restrict IAM policies to least-privilege access. This approach provides a scalable, serverless pipeline for document processing with minimal infrastructure management.