Awesome
Deploying a Multi-Model and Multi-RAG Powered Chatbot Using AWS CDK on AWS
This solution provides ready-to-use code so you can start experimenting with a variety of Large Language Models and Multimodal Language Models, settings and prompts in your own AWS account.
Supported model providers:
- Amazon Bedrock
- Amazon SageMaker self-hosted models from Foundation, Jumpstart and HuggingFace.
- Third-party providers via API such as Anthropic, Cohere, AI21 Labs, OpenAI, etc. See available langchain integrations for a comprehensive list.
Additional Resources
| Resource | Description |
|---|---|
| Secure Messenger GenAI Chatbot | A messenger built on Wickr that can interface with this chatbot to provide Q&A service in tightly regulated environments (i.e. HIPAA). |
| Project Lakechain | A powerful cloud-native, AI-powered, document (docs, images, audios, videos) processing framework built on top of the AWS CDK. |
| AWS Generative AI CDK Constructs | Open-source library extension of the AWS Cloud Development Kit (AWS CDK) aimed to help developers build generative AI solutions using pattern-based definitions for their architecture. |
| Artifacts and Tools for Bedrock | An innovative chat-based user interface with support for tools and artifacts. It can create graphs and diagrams, analyze data, write games, create web pages, generate files, and much more. |
Roadmap
Roadmap is available through the GitHub Project
Authors
Contributors
License
This library is licensed under the MIT-0 License. See the LICENSE file.
- License of the project.
- Code of Conduct of the project.
- CONTRIBUTING for more information.
Although this repository is released under the MIT-0 license, its front-end and SQL implementation use the following third party projects:
These projects' licensing includes the LGPL v3 and BlueOak-1.0.0 licenses.
Legal Disclaimer
You should consider doing your own independent assessment before using the content in this sample for production purposes. This may include (amongst other things) testing, securing, and optimizing the content provided in this sample, based on your specific quality control practices and standards.