Quick Start

This guide will help you deploy your first GenAI IDP solution in under 30 minutes. We'll deploy a complete document processing pipeline with web UI, assessment, and evaluation capabilities.

What You'll Deploy

This quick start creates:

S3 Buckets: For document input, output, and working storage
Processing Pipeline: Step Functions workflow with Lambda functions
AI Processing: Amazon Bedrock integration for document analysis
Web UI: CloudFront-distributed interface with Cognito authentication
Assessment Functions: Document quality measurement
Evaluation System: Baseline comparison capabilities
Analytics: Reporting environment with Athena integration
Monitoring: CloudWatch dashboards and comprehensive logging

Step 1: Choose Your Processor

Navigate to the Bedrock LLM processor example (recommended for beginners):

cd examples/bedrock-llm-processor

Step 2: Configure Variables

Copy the example configuration and customize it:

cp terraform.tfvars.example terraform.tfvars

Edit terraform.tfvars with your configuration:

# terraform.tfvars
# Basic Configuration
region = "us-east-1"
prefix = "genai-idp"

# Administrator Configuration
admin_email = "admin@example.com"

# Logging Configuration
log_level          = "INFO"
log_retention_days = 7

# Data Retention
data_tracking_retention_days = 365

# Core Processing Models
classification_model_id = "us.amazon.nova-pro-v1:0"
extraction_model_id     = "us.amazon.nova-pro-v1:0"

# Summarization Feature
summarization_enabled  = true
summarization_model_id = "us.anthropic.claude-3-7-sonnet-20250219-v1:0"

# Evaluation Feature
enable_evaluation   = false
evaluation_model_id = "anthropic.claude-3-sonnet-20240229-v1:0"

# Assessment Feature
enable_assessment = false

# Reporting Feature
enable_reporting = false

# API Configuration
enable_api = true

# Web UI Configuration
web_ui = {
  enabled = true
}

# Tags
tags = {
  Environment = "dev"
  Project     = "genai-idp-accelerator"
}

Model Access Required

Before deployment, ensure you have access to the required Bedrock models in the AWS Console:

Navigate to Amazon Bedrock → Model access
Request access to: Claude 3 Sonnet, Claude 3 Haiku, Nova Pro, and Titan Text Express

Step 3: Deploy the Infrastructure

Initialize and deploy:

# Initialize Terraform
terraform init

# Review the deployment plan
terraform plan

# Deploy the infrastructure
terraform apply

When prompted, type yes to confirm the deployment.

Deployment Time

The initial deployment typically takes 10-15 minutes to complete due to Lambda layer building and CloudFront distribution setup.

Step 4: Access Your Deployment

After deployment completes, get the important URLs:

# View all outputs
terraform output

# Get the Web UI URL
echo "Web UI: $(terraform output -raw web_ui_url)"

# Get the input bucket name
echo "Upload documents to: $(terraform output -raw input_bucket_name)"

Step 5: Test the Pipeline

Access the Web UI

Open the Web UI URL from the terraform output
Sign up with your admin email (if configured)
Upload a test document through the interface

Or Upload via CLI

# Create a simple test document
echo "Invoice #12345
Date: 2024-01-15
Customer: Acme Corp
Amount: $1,250.00
Description: Professional services" > test-invoice.txt

# Upload to the input bucket
INPUT_BUCKET=$(terraform output -raw input_bucket_name)
aws s3 cp test-invoice.txt s3://$INPUT_BUCKET/

Monitor Processing

Watch the processing in real-time:

# Stream processing logs
aws logs tail /aws/lambda/$(terraform output -raw prefix)-processing --follow

# Or check the Web UI for real-time status updates

Check Results

After processing completes (usually 1-2 minutes):

# List processed results
OUTPUT_BUCKET=$(terraform output -raw output_bucket_name)
aws s3 ls s3://$OUTPUT_BUCKET/processed/

# Download results
aws s3 cp s3://$OUTPUT_BUCKET/processed/ ./results/ --recursive

Step 6: Explore the Features

Assessment Results

If you enabled assessment, check the quality scores:

# View assessment results
cat ./results/*/assessment.json

Analytics Data

If you enabled reporting, query analytics with Athena:

-- Example query in Athena console
SELECT 
    document_id,
    processing_time_ms,
    accuracy_score,
    cost_usd
FROM document_processing_metrics 
WHERE year = 2024 AND month = 1
LIMIT 10;

Web UI Features

Explore the web interface:

Document upload and management
Real-time processing status
Results visualization
Configuration management

Understanding Your Deployment

Your deployment includes:

Processing Pipeline: Step Functions workflow with Lambda functions
Storage: Input, output, and working S3 buckets
AI Processing: Bedrock integration with Nova Pro and Claude models
Web Interface: CloudFront distribution with Cognito authentication
Monitoring: CloudWatch dashboards and comprehensive logging
Analytics: Glue database with Athena-ready tables (if enabled)

Cleanup

When you're done testing, clean up the resources:

# Empty S3 buckets first (required)
aws s3 rm s3://$(terraform output -raw input_bucket_name) --recursive
aws s3 rm s3://$(terraform output -raw output_bucket_name) --recursive
aws s3 rm s3://$(terraform output -raw working_bucket_name) --recursive

# Destroy all resources
terraform destroy

Type yes when prompted to confirm the destruction.

Data Loss

This will permanently delete all resources and data. Make sure to backup any important documents or results before running destroy.

Next Steps

Now that you have a working deployment:

Explore Examples: Try the BDA Processor or SageMaker UDOP
Customize Processing: Modify prompts and models for your use case
Scale Up: Adjust concurrency and performance settings
Production Setup: Review deployment guides for production considerations

Troubleshooting

Common Issues

Model Access Denied

Error: AccessDeniedException: You don't have access to the model

Solution: Enable model access in Bedrock console before deployment

Bucket Name Conflicts

Error: BucketAlreadyExists

Solution: Change the prefix variable to something unique

Lambda Layer Build Failures

Error: Failed to build Lambda layer

Solution: Ensure Docker is running and try rebuilding the Lambda layers

Getting Help

If you encounter issues:

Check CloudWatch logs for detailed error information
Review troubleshooting guide
Consult FAQs
Open an issue in the repository

Summary

You've successfully deployed a complete GenAI IDP solution with:

✅ Multi-stage AI processing with Amazon Bedrock
✅ Web UI for document management
✅ Assessment functions for quality measurement
✅ Evaluation system for accuracy tracking
✅ Analytics environment for insights
✅ Scalable architecture ready for production use

Ready to build more complex solutions? Explore our examples and advanced deployment guides!