Pattern 2: Bedrock Classification and Extraction

⚠️ DEPRECATED: Pattern 2 has been superseded by the Unified Pattern, which combines both BDA and pipeline processing modes into a single deployment. The use_bda configuration flag (set via the UI) controls whether documents are processed via BDA or the step-by-step pipeline. See architecture.md for details on the unified architecture.

This document is retained as a reference for pipeline-specific concepts (OCR, classification, extraction, assessment, rule validation) that still apply when use_bda: false is set in the unified pattern (the default mode).

This pattern implements an intelligent document processing workflow that uses Amazon Bedrock with Nova or Claude models for both page classification/grouping and information extraction.

Architecture Overview
Bedrock OCR Feature
Customizing Classification
Few Shot Example Feature
Customizing Extraction
Assessment Feature
Testing
Best Practices

Architecture Overview

The workflow consists of three main processing steps with an optional assessment step:

OCR processing using Amazon Textract
Document classification using Claude via Amazon Bedrock (with two available methods):
- Page-level classification: Classifies individual pages and groups them
- Holistic packet classification: Analyzes multi-document packets to identify document boundaries
Field extraction using Claude via Amazon Bedrock
Assessment (optional): Confidence evaluation of extraction results using LLMs

State Machine Workflow

The Step Functions state machine (workflow.asl.json) orchestrates the following flow:

OCRStep → ClassificationStep → ProcessPageGroups (Map State for Extraction) → ProcessResultsStep

Each step includes comprehensive retry logic for handling transient errors:

Initial retry after 2 seconds
Exponential backoff with rate of 2
Maximum of 8-10 retry attempts depending on the step

Lambda Functions

OCR Function

Purpose: Processes input PDFs using Amazon Textract or Amazon Bedrock
Key Features:
- Supports two OCR backends:
  - Amazon Textract (default)
  - Amazon Bedrock LLMs (Claude, Nova)
- Concurrent page processing with ThreadPoolExecutor
- Configurable Image Processing: Enhanced image resizing with aspect-ratio preservation
- Configurable DPI: Adjustable DPI for PDF-to-image conversion
- Dual Image Strategy: Stores original high-DPI images while using resized images for OCR processing
- Smart Resizing: Only downsizes images when necessary (scale factor < 1.0)
- Image preprocessing and optimization
- Comprehensive error handling and retries
- Detailed metrics tracking

Input:

{
  "execution_arn": "<ARN>",
  "output_bucket": "<BUCKET>",
  "input": {
    "detail": {
      "bucket": { "name": "<BUCKET>" },
      "object": { "key": "<KEY>" }
    }
  }
}

Output:

{
  "metadata": {
    "input_bucket": "<BUCKET>",
    "object_key": "<KEY>",
    "output_bucket": "<BUCKET>",
    "output_prefix": "<PREFIX>",
    "num_pages": "<NUMBER OF PAGES>"
  },
  "pages": {
    "<PAGE_NUMBER>": {
      "rawTextUri": "<S3_URI>",
      "parsedTextUri": "<S3_URI>",
      "imageUri": "<S3_URI>"
    }
  }
}

Classification Function

Purpose: Classifies pages or document packets using Claude via Bedrock and segments into sections
Key Features:
- Two classification methods:
  - Page-level classification (multimodalPageLevelClassification)
  - Holistic packet classification (textbasedHolisticClassification)
- RVL-CDIP dataset categories for classification
- Concurrent page processing
- Automatic image resizing and optimization
- Robust error handling with exponential backoff
- Few shot example support for improved accuracy
Input: Output from OCR function

Output:

{
  "metadata": "<FROM_OCR>",
  "sections": [
    {
      "id": "<GROUP_ID>",
      "class": "<CLASS>",
      "pages": [...]
    }
  ]
}

Extraction Function

Purpose: Extracts fields using Claude via Bedrock
Key Features:
- Document class-specific attribute extraction
- Configurable extraction attributes
- Comprehensive error handling
- Token usage tracking
- Few shot example support for improved accuracy
Input: Individual section from Classification output

Output:

{
  "section": {
    "id": "<ID>",
    "class": "<CLASS>",
    "page_ids": ["<PAGEID>", ...],
    "outputJSONUri": "<S3_URI>"
  },
  "pages": [...]
}

ProcessResults Function

Purpose: Consolidates results from all sections

Output: Standardized format for GenAIIDP parent stack:

{
  "Sections": [{
    "Id": "<ID>",
    "PageIds": ["<PAGEID>", ...],
    "Class": "<CLASS>",
    "OutputJSONUri": "<S3_URI>"
  }],
  "Pages": [{
    "Id": "<ID>",
    "Class": "<CLASS>",
    "TextUri": "<S3_URI>",
    "ImageUri": "<S3_URI>"
  }],
  "PageCount": "<TOTAL_PAGES>"
}

Human-in-the-Loop (HITL)

Pattern-2 supports Human-in-the-Loop (HITL) review capabilities using Amazon SageMaker Augmented AI (A2I). This feature allows human reviewers to validate and correct extracted information when the system’s confidence falls below a specified threshold.

Pattern-2 Specific Configuration:

EnableHITL: Boolean parameter to enable/disable the HITL feature
IsPattern2HITLEnabled: Boolean parameter specific to Pattern-2 HITL enablement
Pattern2 - Existing Private Workforce ARN: Optional parameter to use existing private workforce

For comprehensive HITL documentation including workflow details, configuration steps, best practices, and troubleshooting, see the Human-in-the-Loop Review Guide.

Monitoring and Metrics

The pattern includes a comprehensive CloudWatch dashboard with:

Performance Metrics

Document and page throughput
Token usage (input/output/total)
Bedrock request statistics
Processing latencies
Throttling and retry metrics

Error Tracking

Lambda function errors
Long-running invocations
Classification/extraction failures
Throttling events

Lambda Function Metrics

Duration
Memory usage
Error rates
Concurrent executions

Template Outputs

The pattern exports these outputs to the parent stack:

StateMachineName: Name of Step Functions state machine
StateMachineArn: ARN of Step Functions state machine
StateMachineLogGroup: CloudWatch log group for state machine
DashboardName: Name of pattern-specific dashboard
DashboardArn: ARN of pattern-specific dashboard

Configuration

Stack Deployment Parameters:

ClassificationMethod: Classification methodology to use (options: ‘multimodalPageLevelClassification’ or ‘textbasedHolisticClassification’)
Summarization: Control summarization via configuration file summarization.enabled property (replaces IsSummarizationEnabled parameter)
ConfigurationDefaultS3Uri: Optional S3 URI to custom configuration (uses default configuration if not specified)
MaxConcurrentWorkflows: Workflow concurrency limit
LogRetentionDays: CloudWatch log retention period
ExecutionTimeThresholdMs: Latency threshold for alerts

Configuration Management:

Model selection is now handled through configuration files rather than CloudFormation parameters
Configuration supports multiple presets per pattern (e.g., default, checkboxed_attributes_extraction, medical_records_summarization, few_shot_example)
Configuration can be updated through the Web UI without stack redeployment
Model choices are constrained through enum constraints in the configuration schema

Bedrock OCR Feature

Overview

Pattern 2 now supports Amazon Bedrock LLMs (Claude, Nova) as an alternative OCR backend alongside the traditional Amazon Textract service. This feature enables multimodal document processing where large language models can extract text from document images using their vision capabilities.

Configuration

Bedrock OCR is configured through the pattern’s configuration files. The OCR backend can be selected using the backend parameter:

ocr:
  backend: "bedrock"  # Options: "textract", "bedrock", "none"
  model_id: "us.anthropic.claude-3-7-sonnet-20250219-v1:0"
  system_prompt: "You are an expert OCR system. Extract all text from the provided image accurately, preserving layout where possible."
  task_prompt: "Extract all text from this document image. Preserve the layout, including paragraphs, tables, and formatting."

Enabling Bedrock OCR

To use Bedrock OCR:

Set the backend: Configure backend: "bedrock" in your OCR configuration
Choose a model: Select from supported vision-capable models:
- us.amazon.nova-lite-v1:0
- us.amazon.nova-pro-v1:0
- us.amazon.nova-premier-v1:0
- us.amazon.nova-2-lite-v1:0
- us.anthropic.claude-3-haiku-20240307-v1:0
- us.anthropic.claude-haiku-4-5-20251001-v1:0
- us.anthropic.claude-3-5-sonnet-20241022-v2:0
- us.anthropic.claude-3-7-sonnet-20250219-v1:0
- us.anthropic.claude-sonnet-4-20250514-v1:0
- us.anthropic.claude-sonnet-4-5-20250929-v1:0
- us.anthropic.claude-sonnet-4-6
- us.anthropic.claude-sonnet-4-6:1m
- us.anthropic.claude-opus-4-20250514-v1:0
- us.anthropic.claude-opus-4-1-20250805-v1:0
- us.anthropic.claude-opus-4-5-20251101-v1:0
- us.anthropic.claude-opus-4-6-v1
- us.anthropic.claude-opus-4-6-v1:1m
- us.anthropic.claude-opus-4-7
- us.anthropic.claude-opus-4-7:1m
- us.anthropic.claude-opus-4-8
- us.anthropic.claude-opus-4-8:1m
- eu.amazon.nova-lite-v1:0
- eu.amazon.nova-pro-v1:0
- eu.amazon.nova-2-lite-v1:0
- eu.anthropic.claude-3-haiku-20240307-v1:0
- eu.anthropic.claude-haiku-4-5-20251001-v1:0
- eu.anthropic.claude-3-5-sonnet-20241022-v2:0
- eu.anthropic.claude-3-7-sonnet-20250219-v1:0
- eu.anthropic.claude-sonnet-4-20250514-v1:0
- eu.anthropic.claude-sonnet-4-5-20250929-v1:0
- eu.anthropic.claude-sonnet-4-6
- eu.anthropic.claude-sonnet-4-6:1m
- eu.anthropic.claude-opus-4-5-20251101-v1:0
- eu.anthropic.claude-opus-4-6-v1
- eu.anthropic.claude-opus-4-6-v1:1m
- eu.anthropic.claude-opus-4-7
- eu.anthropic.claude-opus-4-7:1m
- eu.anthropic.claude-opus-4-8
- eu.anthropic.claude-opus-4-8:1m
- qwen.qwen3-vl-235b-a22b
- global.amazon.nova-2-lite-v1:0
- global.anthropic.claude-haiku-4-5-20251001-v1:0
- global.anthropic.claude-sonnet-4-5-20250929-v1:0
- global.anthropic.claude-sonnet-4-6
- global.anthropic.claude-sonnet-4-6:1m
- global.anthropic.claude-opus-4-5-20251101-v1:0
- global.anthropic.claude-opus-4-6-v1
- global.anthropic.claude-opus-4-6-v1:1m
- global.anthropic.claude-opus-4-7
- global.anthropic.claude-opus-4-7:1m
- global.anthropic.claude-opus-4-8
- global.anthropic.claude-opus-4-8:1m
Configure prompts: Customize system and task prompts for your specific use case
Deploy: The configuration can be updated through the Web UI without stack redeployment

Benefits

Advantages of Bedrock OCR:

Multimodal Understanding: LLMs can understand both visual layout and textual content
Context Awareness: Better handling of complex document structures and relationships
Flexibility: Customizable prompts for domain-specific terminology and formats
Advanced Reasoning: Can handle challenging cases like handwritten text, poor quality scans, or complex layouts
Unified Processing: Same models used for OCR, classification, and extraction provide consistency

When to Use Bedrock OCR:

Documents with complex layouts or mixed content types
Handwritten or low-quality documents where Textract struggles
Domain-specific documents requiring contextual understanding
When you want unified processing across the entire pipeline
For experimental or specialized use cases requiring prompt customization

Cost Considerations

Bedrock OCR Pricing:

Charged per input/output token rather than per page
Typically higher cost per page than Textract for standard documents
Cost varies significantly by model (Nova Lite < Nova Pro < Claude models)
Image tokens are more expensive than text tokens

Cost Optimization Tips:

Model Selection: Use Nova Lite for cost-sensitive applications, Claude for quality-critical use cases
Image Preprocessing: Enable image resizing and preprocessing to reduce token consumption
Prompt Optimization: Use concise, focused prompts to minimize token usage
Hybrid Approach: Use Textract for standard documents, Bedrock for complex cases
Batch Processing: Process multiple pages efficiently with concurrent processing

OCR Configuration

The OCR service in Pattern 2 supports enhanced image processing capabilities for optimal text extraction:

DPI Configuration

Configure DPI (Dots Per Inch) for PDF-to-image conversion:

# Example OCR service initialization with custom DPI
ocr_service = OcrService(
    dpi=400,  # Higher DPI for better quality (default: 300)
    resize_config={
        'target_width': 1200,
        'target_height': 1600
    }
)

Image Resizing Configuration

The OCR service supports optional image resizing for processing optimization:

# OCR configuration example
ocr:
  image:
    resize_config:
      target_width: 951   # Target width for processing
      target_height: 1268 # Target height for processing
    preprocessing: true   # Enable adaptive binarization preprocessing

OCR Image Processing Features

Configurable DPI: Higher DPI (400+) for better quality, standard DPI (300) for balanced performance
Dual Image Strategy:
- Stores original high-DPI images in S3 for archival and downstream processing
- Uses resized images for OCR processing to optimize performance
Aspect Ratio Preservation: Images are resized proportionally without distortion
Smart Scaling: Only downsizes images when necessary (scale factor < 1.0)
Image Preprocessing: Optional adaptive binarization to improve OCR accuracy on challenging documents
Enhanced Logging: Detailed logging for DPI and resize operations

Image Preprocessing Configuration

The OCR service supports optional adaptive binarization preprocessing to improve OCR accuracy on challenging documents:

# Enable preprocessing for improved OCR accuracy
ocr:
  image:
    preprocessing: true  # Enable adaptive binarization

Adaptive Binarization Benefits:

Improved OCR Accuracy: Significantly enhances text extraction on documents with uneven lighting, shadows, or low contrast
Background Noise Reduction: Removes background gradients and noise that can interfere with OCR
Enhanced Edge Detection: Sharpens text boundaries for better character recognition
Robust Processing: Handles challenging document conditions like poor scans or faded text

When to Enable Preprocessing:

Documents with uneven lighting or shadows
Low contrast text or faded documents
Scanned documents with background noise
Handwritten or mixed content documents
When standard OCR accuracy is insufficient

Configuration Benefits

Quality Control: Higher DPI settings improve OCR accuracy for complex documents
Performance Optimization: Resized images reduce processing time and memory usage
Storage Efficiency: Dual strategy balances quality preservation with processing efficiency
Preprocessing Enhancement: Adaptive binarization improves OCR accuracy on challenging documents
Flexibility: Runtime configuration allows adjustment without code changes
Backward Compatibility: Default values maintain existing behavior

Best Practices for OCR

DPI Selection:
- Use 300 DPI for standard documents
- Use 400+ DPI for documents with small text or complex layouts
- Consider processing costs when using higher DPI settings
Image Resizing:
- Enable resizing for large documents to improve processing speed
- Maintain aspect ratios to preserve text readability
- Test different dimensions based on document types
Performance Tuning:
- Monitor processing times and adjust DPI/resize settings accordingly
- Use concurrent processing for multi-page documents
- Balance quality requirements with processing costs

Customizing Classification

The pattern supports two different classification methods:

Page-Level Classification (multimodalPageLevelClassification): This is the default method that classifies each page independently based on its visual layout and textual content. It outputs a simple JSON format with a single class label per page.
Holistic Packet Classification (textbasedHolisticClassification): This method examines the document as a whole to identify boundaries between different document types within a multi-document packet. It can detect logical document boundaries and identifies document types in the context of the whole document. This is especially useful for packets where individual pages may not be clearly classifiable on their own. It outputs a JSON format that identifies document segments with start and end page numbers.

You can select which method to use by setting the ClassificationMethod parameter when deploying the stack.

The classification system uses RVL-CDIP dataset categories and can be customized through the configuration files. Classification models and prompts are now managed through the configuration library rather than CloudFormation parameters.

Available categories:

letter
form
email
handwritten
advertisement
scientific_report
scientific_publication
specification
file_folder
news_article
budget
invoice
presentation
questionnaire
resume
memo

Few Shot Example Feature

Pattern 2 supports few shot learning through example-based prompting to significantly improve classification and extraction accuracy. This feature allows you to provide concrete examples of documents with their expected classifications and attribute extractions.

Overview

Few shot examples work by including reference documents with known classifications and expected attribute values in the prompts sent to the AI model. This helps the model understand the expected format and accuracy requirements for your specific use case.

Configuration

Few shot examples are configured using JSON Schema format in the configuration files in the config_library/unified/ directory. The few_shot_example configuration demonstrates how to set up examples:

classes:
  - $schema: "https://json-schema.org/draft/2020-12/schema"
    $id: Letter
    x-aws-idp-document-type: Letter
    type: object
    description: "A formal written correspondence..."
    properties:
      SenderName:
        type: string
        description: "The name of the person who wrote the letter..."
    x-aws-idp-examples:
      - x-aws-idp-class-prompt: "This is an example of the class 'Letter'"
        name: "Letter1"
        x-aws-idp-attributes-prompt: |
          expected attributes are:
              "SenderName": "Will E. Clark",
              "SenderAddress": "206 Maple Street P.O. Box 1056 Murray Kentucky 42071-1056",
              "RecipientName": "The Honorable Wendell H. Ford"
        x-aws-idp-image-path: "config_library/unified/few_shot_example/example-images/letter1.jpg"
  - $schema: "https://json-schema.org/draft/2020-12/schema"
    $id: Email
    x-aws-idp-document-type: Email
    type: object
    description: "A digital message with email headers..."
    x-aws-idp-examples:
      - x-aws-idp-class-prompt: "This is an example of the class 'Email'"
        name: "Email1"
        x-aws-idp-attributes-prompt: |
          expected attributes are:
             "FromAddress": "Kelahan, Ben",
             "ToAddress": "TI New York: 'TI Minnesota",
             "Subject": "FW: Morning Team Notes 4/20"
        x-aws-idp-image-path: "config_library/unified/few_shot_example/example-images/email1.jpg"

Benefits

Using few shot examples provides several advantages:

Improved Accuracy: Models perform better when given concrete examples
Consistent Formatting: Examples help ensure consistent output structure
Domain Adaptation: Examples help models understand domain-specific terminology
Reduced Hallucination: Examples reduce the likelihood of made-up data
Better Edge Case Handling: Examples can demonstrate how to handle unusual cases

Integration with Template Prompts

The few shot examples are automatically integrated into the classification and extraction prompts using the {FEW_SHOT_EXAMPLES} placeholder. You can also use the {DOCUMENT_IMAGE} placeholder for precise image positioning:

Standard Template with Text Only:

# In classification task_prompt
task_prompt: |
  Classify this document into exactly one of these categories:
  {CLASS_NAMES_AND_DESCRIPTIONS}

  <few_shot_examples>
  {FEW_SHOT_EXAMPLES}
  </few_shot_examples>

  <document_ocr_data>
  {DOCUMENT_TEXT}
  </document_ocr_data>

# In extraction task_prompt
task_prompt: |
  Extract attributes from this document.

  <few_shot_examples>
  {FEW_SHOT_EXAMPLES}
  </few_shot_examples>


  <document_ocr_data>
  {DOCUMENT_TEXT}
  </document_ocr_data>

Enhanced Template with Image Placement:

# In classification task_prompt with image positioning
task_prompt: |
  Classify this document into exactly one of these categories:
  {CLASS_NAMES_AND_DESCRIPTIONS}

  <few_shot_examples>
  {FEW_SHOT_EXAMPLES}
  </few_shot_examples>

  Now examine this new document:
  {DOCUMENT_IMAGE}

  <document_ocr_data>
  {DOCUMENT_TEXT}
  </document_ocr_data>

  Classification:

# In extraction task_prompt with image positioning
task_prompt: |
  Extract attributes from this {DOCUMENT_CLASS} document:
  {ATTRIBUTE_NAMES_AND_DESCRIPTIONS}

  <few_shot_examples>
  {FEW_SHOT_EXAMPLES}
  </few_shot_examples>

  Analyze this document image:
  {DOCUMENT_IMAGE}

  <document_ocr_data>
  {DOCUMENT_TEXT}
  </document_ocr_data>

  Extract as JSON:

Available Template Placeholders

Pattern 2 supports several placeholders for building dynamic prompts:

{CLASS_NAMES_AND_DESCRIPTIONS}: List of document classes and their descriptions
{FEW_SHOT_EXAMPLES}: Examples from the configuration (class-specific for extraction, all classes for classification)
{DOCUMENT_TEXT}: OCR-extracted text content from the document
{DOCUMENT_IMAGE}: Document image(s) positioned at specific locations in the prompt
{DOCUMENT_CLASS}: The classified document type (used in extraction prompts)
{ATTRIBUTE_NAMES_AND_DESCRIPTIONS}: List of attributes to extract with their descriptions

Image Placement Benefits:

Visual Context: Position images where they provide maximum context for the task
Multimodal Understanding: Help models correlate visual and textual information effectively
Flexible Design: Create prompts that flow naturally between different content types
Enhanced Accuracy: Strategic image placement can improve both classification and extraction performance

Using Few Shot Examples

To use few shot examples in your deployment:

Use the example configuration: Deploy with ConfigurationDefaultS3Uri pointing to config_library/unified/few_shot_example/config.yaml
Create custom examples: Copy the example configuration and modify it with your own document examples
Provide example images: Place example document images in the appropriate directory and reference them in the imagePath field

Best Practices

Quality over Quantity: Use 1-3 high-quality examples per document class
Representative Examples: Choose examples that represent typical documents in your use case
Clear Attribution: Ensure examples clearly show expected attribute extractions
Diverse Coverage: Include examples that cover different variations and edge cases

Customizing Extraction

The extraction system can be customized through the configuration files rather than CloudFormation parameters:

Attribute Definitions:
- Define attributes per document class in the classes section of the configuration
- Specify descriptions for each attribute
- Configure the format and structure
Extraction Prompts:
- Customize system behavior through the system_prompt in configuration
- Add domain expertise and guidance in the task_prompt
- Modify output formatting requirements
Model Selection:
- Model selection is handled through enum constraints in the configuration
- Available models are defined in the configuration schema
- Changes can be made through the Web UI without redeployment

Example attribute definition from the configuration using JSON Schema format:

classes:
  - $schema: "https://json-schema.org/draft/2020-12/schema"
    $id: Invoice
    x-aws-idp-document-type: Invoice
    type: object
    description: A commercial document issued by a seller to a buyer relating to a sale
    properties:
      InvoiceNumber:
        type: string
        description: The unique identifier for the invoice. Look for 'invoice no', 'invoice #', or 'bill number', typically near the top of the document.
      InvoiceDate:
        type: string
        description: The date when the invoice was issued. May be labeled as 'date', 'invoice date', or 'billing date'.
      TotalAmount:
        type: string
        description: The final amount to be paid including all charges. Look for 'total', 'grand total', or 'amount due', typically the last figure on the invoice.

Confidence (Assessment)

As of config v0.6, per-field confidence and geometry are outputs of extraction, not a separate downstream stage. Their settings live under extraction.confidence.* and extraction.geometry.* (there is no top-level assessment.{model, geometry_mode, ...} block anymore). This section is a Pattern 2 (Pipeline mode) summary; the full reference is Extraction & Confidence.

Overview

Confidence assessment produces, for each extracted attribute:

Confidence Scores: Per-attribute confidence ratings (0.0-1.0)
Explanatory Reasoning: Human-readable explanations for each confidence score
UI Integration: Automatic display in the web interface visual editor
Cost Optimization: Can be disabled entirely for zero LLM cost
Large-list batching: Long list fields (hundreds of rows) are scored in sequential batches so every row is covered

Confidence modes

extraction.confidence.mode controls where per-field confidence is produced:

separate (default) — on the Simple (non-agentic) path, confidence runs as the standalone Assessment step; on the Advanced (agentic) path, it runs as a second inference inside each extraction shard and the standalone step auto-skips.
integrated — a single extraction inference returns values and inline confidence together (works on both the simple and agentic paths); the service splits the envelope, enriches with thresholds, and emits explainability_info, so the standalone Assessment step auto-skips.
off — no confidence scoring (equivalent to enabled: false); zero LLM cost.

extraction:
  confidence:
    enabled: true             # false disables confidence entirely (zero LLM cost)
    mode: separate            # off | separate (default) | integrated
    model: us.amazon.nova-lite-v1:0
    temperature: 0.0
    list_batch_size: 25       # rows per assessment batch for large lists
    system_prompt: "You are an expert document analyst..."
    task_prompt: |
      Assess the confidence of extraction results for this {DOCUMENT_CLASS} document.
      Extraction Results: {EXTRACTION_RESULTS}
      Attributes: {ATTRIBUTE_NAMES_AND_DESCRIPTIONS}
      Document Text: {DOCUMENT_TEXT}
      OCR Confidence: {OCR_TEXT_CONFIDENCE}
      {DOCUMENT_IMAGE}

Behavior when disabled (enabled: false or mode: off): the assessment Lambda is still invoked (minimal overhead) but returns immediately with logging “Assessment is disabled via configuration” — no LLM calls, no S3 operations. Defaults to enabled when the property is missing.

Migration note: The previous IsAssessmentEnabled CloudFormation parameter has been removed in favor of this configuration-based control.

Large lists (`list_batch_size`)

For documents with large lists (bank statements with hundreds of transactions, line-item tables, brokerage holdings), the standalone Assessment step batches large lists automatically: it slices the largest list field into extraction.confidence.list_batch_size chunks (default 25), assesses each chunk sequentially with the shared scalars/context, then reconciles the results so every list cell gets its own confidence and bounding box. A bounded missing-row retry re-scores any rows the model dropped, so coverage reaches 100%. Lower list_batch_size if a model still struggles to enumerate a full chunk; raise it to reduce the number of inference calls.

Granular assessment is retired. The former “granular assessment” service (a thread-pool fan-out with DynamoDB caching, formerly configured under assessment.granular / extraction.confidence.granular) has been retired and deleted. Large-list batching is its full replacement. Any leftover granular.* keys still validate but are ignored — no config edit required. See Granular Assessment Retirement.

For large documents, prefer Advanced (agentic) extraction

For very large or complex documents, prefer Advanced (agentic) extraction — it shards both extraction and confidence assessment and produced the best-calibrated confidence in testing. Simple + separate remains fully viable for large lists via the batching above.

Comprehensive Documentation

For detailed information about confidence and geometry configuration, output formats, confidence thresholds, UI integration, cost optimization, and troubleshooting, see Extraction & Confidence. That guide covers:

Complete configuration examples and placeholders
Attribute types and assessment formats (simple, group, list)
Confidence threshold configuration and UI integration
Confidence modes (off / separate / integrated) and geometry modes
Large-list batching and the missing-row retry
Cost optimization strategies and token reduction techniques
Multimodal assessment with image processing
Best practices

Best Practices

Configuration Management:
- Use the configuration library for different use cases (default, medical_records, few_shot_example)
- Test configuration changes thoroughly before production deployment
- Leverage the Web UI for configuration updates without redeployment
Throttling Management:
- Implement exponential backoff with jitter
- Configure appropriate retry limits
- Monitor throttling metrics
Error Handling:
- Comprehensive error logging
- Graceful degradation
- Clear error messages
Performance Optimization:
- Concurrent processing where appropriate
- Image optimization
- Resource pooling
Monitoring:
- Detailed CloudWatch metrics
- Performance dashboards
- Error tracking
Security:
- KMS encryption
- Least privilege IAM roles
- Secure configuration management
Few Shot Examples:
- Use high-quality, representative examples
- Include examples for all document classes you expect to process
- Regularly review and update examples based on real-world performance
- Test configurations with examples before production deployment
Image Processing Optimization:
- Configure appropriate image dimensions for each service based on document complexity
- Use higher DPI (400+) for OCR when processing documents with small text or complex layouts
- Balance image quality with processing performance and costs
- Test different image configurations with your specific document types
- Monitor memory usage and processing times when adjusting image settings
- Leverage the dual image strategy in OCR to preserve quality while optimizing processing

Pattern 2: Bedrock Classification and Extraction

Pattern 2: Bedrock Classification and Extraction

Table of Contents

Architecture Overview

State Machine Workflow

Lambda Functions

OCR Function

Classification Function

Extraction Function

ProcessResults Function

Human-in-the-Loop (HITL)

Monitoring and Metrics

Performance Metrics

Error Tracking

Lambda Function Metrics

Template Outputs

Configuration

Bedrock OCR Feature

Overview

Configuration

Enabling Bedrock OCR

Benefits

Cost Considerations

OCR Configuration

DPI Configuration

Image Resizing Configuration

OCR Image Processing Features

Image Preprocessing Configuration

Configuration Benefits

Best Practices for OCR

Customizing Classification

Few Shot Example Feature

Overview

Configuration

Benefits

Integration with Template Prompts

Available Template Placeholders

Using Few Shot Examples

Best Practices

Customizing Extraction

Confidence (Assessment)

Overview

Confidence modes

Large lists (list_batch_size)

For large documents, prefer Advanced (agentic) extraction

Comprehensive Documentation

Best Practices

Large lists (`list_batch_size`)