To use Amazon Bedrock in a document processing workflow, you can build an automated pipeline that extracts text from documents stored in S3, processes it with a foundation model (FM) like Anthropic Claude, and saves the results. Here's a step-by-step breakdown:
1. Extract and Prepare Document Data When a document is uploaded to an S3 bucket, trigger an AWS Lambda function. The Lambda function uses Amazon Textract to extract text from PDFs, images, or other formats. For text files, read the content directly. Preprocess the extracted text to remove unnecessary formatting, split large documents into manageable chunks (if exceeding the FM's token limit), and ensure the input meets Bedrock's requirements. For example, Claude supports up to 100,000 tokens, so chunking may only be needed for exceptionally long documents.
2. Process Text with Bedrock
Invoke Bedrock's InvokeModel API from the Lambda function to send the prepared text to a model like anthropic.claude-v2. Configure the request with a prompt specifying your task, such as "Summarize this document in 3 bullet points focusing on key decisions." Handle API rate limits by implementing retries with exponential backoff. For large-scale workflows, consider using asynchronous processing with a queue (Amazon SQS) and separate worker functions to decouple extraction from processing.
3. Store and Track Results
Save the model's output (e.g., summaries) to a designated S3 bucket, using a consistent naming convention like s3://output-bucket/{original_filename}_summary.txt. Optionally, store metadata (timestamp, model version, input file path) in DynamoDB for audit purposes. Implement error logging via CloudWatch to track failed document processing attempts, and use S3 event notifications to trigger downstream workflows like sending summaries via SES email or updating a UI.
Example Architecture
S3 Upload → Lambda (Textract) → Bedrock (Claude) → S3/DynamoDB
Costs are driven by Textract pages processed, Bedrock input/output tokens, and Lambda duration. For production use, add security controls like IAM policies restricting Bedrock access and server-side encryption for S3 data. Test with sample documents to validate prompt effectiveness and output quality before scaling.