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.
Deploy IDEFICS models on Amazon SageMaker and see how the chatbot can answer questions about images, describe visual content, and generate text grounded in multiple images.
Send the same query to 2 to 4 separate models at once and see how each one responds based on its own learned history, context and access to the same powerful document retriever.
A workspace is a logical namespace where you can upload files for indexing and storage in one of the vector databases. You can select the embeddings model and text-splitting configuration of your choice.
The solution comes with several debugging tools to help you debug RAG scenarios.
The repository includes a CDK construct to deploy a full-fledged UI built with React to interact with the deployed LLMs/MLMs as chatbots. Hosted on Amazon S3 and distributed with Amazon CloudFront.
Build multiple chatbots tailored for different domains by selecting underlying ML models, assigning workspaces, and customizing settings - all through an admin interface.
The solution supports restricting access to chatbots and system functionality based on user roles.