BBAWSLightsailMiniAIV1a

AI-enhanced AWS Lightsail Container Service deployment with AWS Bedrock integration.

Overview

BBAWSLightsailMiniAIV1a extends the base BBAWSLightsailMiniV1a stack to add AI capabilities through AWS Bedrock services. It provides infrastructure for AI-powered document processing, embeddings, and chat applications.

Features

• AI-Ready Infrastructure: AWS Bedrock integration for LLM and embedding models
• Document Processing: S3 storage with AI-optimized folder structure
• Training Data Management: Support for URL-based content ingestion
• Containerized Deployment: Lightsail Container Service with AI workloads
• Scalable Storage: S3 buckets for documents, embeddings, and processed content

Installation

pip install BBAWSLightsailMiniAIV1a

Quick Start

from cdktf import App
from BBAWSLightsailMiniAIV1a import BBAWSLightsailMiniAIV1a

app = App()

# Get archetype for configuration
archetype = BBAWSLightsailMiniAIV1a.get_archetype(
    product='ai-app',
    app='smart-docs',
    tier='development',
    organization='your-org',
    region='us-east-1'
)

# Create AI-enhanced stack
ai_stack = BBAWSLightsailMiniAIV1a(
    app, "my-ai-stack",
    
    # Base configuration
    project_name=archetype.get_project_name(),
    environment=archetype.get_tier(),
    region=archetype.get_region(),
    secret_name=archetype.get_secret_name(),
    profile="default",
    
    # AI configuration
    model="anthropic.claude-3-sonnet-20240229-v1:0",
    training_urls=[
        "https://docs.aws.amazon.com/bedrock/",
        "https://your-docs.com"
    ]
)

archetype.set_stack(ai_stack)
app.synth()

Configuration

AI Models

The stack supports various AWS Bedrock models:

• Claude Models: anthropic.claude-3-sonnet-20240229-v1:0, anthropic.claude-3-haiku-20240307-v1:0
• Titan Models: amazon.titan-text-express-v1, amazon.titan-embed-text-v1
• Llama Models: meta.llama2-13b-chat-v1, meta.llama2-70b-chat-v1

Training Data

Configure training URLs for document ingestion:

training_urls = [
    "https://docs.aws.amazon.com/bedrock/",
    "https://your-company.com/api-docs",
    "https://your-company.com/knowledge-base"
]

Chunking Configuration

Customize document processing:

chunk_size = 2000      # Characters per chunk
chunk_overlap = 400    # Character overlap between chunks

Architecture

The AI stack creates:

1. S3 Buckets:

   • documents/ - Raw document storage
   • embeddings/ - Processed embeddings
   • training/ - Training data from URLs

2. IAM Roles:

   • Bedrock access permissions
   • S3 read/write permissions
   • Container service access

3. Lightsail Container Service:

   • AI application deployment
   • Auto-scaling configuration
   • Custom domain support

Examples

Basic Deployment

# See examples/basic_deployment.py

Advanced Configuration

# See examples/advanced_deployment.py

Environment Variables

The following environment variables are available in your container:

• AWS_BEDROCK_MODEL: The configured Bedrock model
• AWS_BEDROCK_EMBEDDING_MODEL: The embedding model
• S3_DOCUMENTS_BUCKET: Documents bucket name
• S3_EMBEDDINGS_BUCKET: Embeddings bucket name
• CHUNK_SIZE: Document chunk size
• CHUNK_OVERLAP: Chunk overlap size

Development

Building the Package

python prepare.py
python setup.py build

Running Tests

pytest tests/

Semantic Versioning

This package uses semantic-release for automated versioning:

semantic-release publish

Architecture Flags

Use architecture flags to customize deployment:

ArchitectureFlags = BBAWSLightsailMiniAIV1a.get_architecture_flags()

flags = [
    ArchitectureFlags.SKIP_DATABASE._value_,  # Skip database for AI-only workload
    # Add other flags as needed
]

Dependencies

• BBAWSLightsailMiniV1a>=1.0.0 - Base Lightsail stack
• cdktf>=0.20.0 - CDK for Terraform
• constructs>=10.0.0 - AWS CDK constructs

License

MIT License - see LICENSE file for details.

Support

For issues and questions:

• GitHub Issues: Repository issues
• Documentation: Full documentation

Changelog

See CHANGELOG.md for version history and changes.