This solution provides a modular and comprehensive chatbot framework for experimenting with multiple Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) use cases on AWS. It targets developers and researchers looking to quickly prototype and deploy generative AI applications with support for various model providers and multimodal capabilities.

How It Works

The chatbot leverages the AWS Cloud Development Kit (CDK) for deployment, enabling a modular architecture that supports multiple LLM providers (Amazon Bedrock, SageMaker, third-party APIs like OpenAI) and RAG integrations. This approach allows users to easily switch between models, configure prompts, and integrate diverse data sources for enhanced contextual responses.

Quick Start & Requirements

• Install/Run: AWS CDK deployment.
• Prerequisites: AWS account, AWS CLI, Node.js, Python 3.8+, Docker.
• Resources: Requires AWS services like Amazon Bedrock, Amazon SageMaker, Lambda, API Gateway, and potentially others depending on configuration.
• Docs: aws-samples/aws-genai-llm-chatbot

Highlighted Details

• Supports a wide array of LLM providers including Amazon Bedrock (Anthropic, Cohere, Mistral, Amazon Nova models), SageMaker, and third-party APIs.
• Includes support for multimodal models, enabling image and video understanding and generation.
• Offers integrations with other AWS generative AI projects like Project Lakechain and AWS Generative AI CDK Constructs.
• Provides a secure messenger integration for regulated environments.

Maintenance & Community

• Maintained by AWS Samples.
• Roadmap available via GitHub Projects.
• CONTRIBUTING guidelines are provided.

Licensing & Compatibility

• MIT-0 License for the core solution.
• Frontend and SQL implementations use third-party projects with LGPL v3 and BlueOak-1.0.0 licenses, which may have implications for commercial use or closed-source linking.

Limitations & Caveats

The solution is provided as a sample for experimentation and requires independent assessment for production use, including testing, security, and optimization. The licensing of third-party components should be carefully reviewed for commercial deployment.