Codename: "Galileo"

All classes are under active development and subject to non-backward compatible changes or removal in any future version. These are not subject to the Semantic Versioning model. This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.

Conversational generative AI applications that provide search and summarisation against a collection of private documents (also known as "retrieval augmented generation" or RAG) are comprised of a number of complex components. These include: an elastic document ingestion pipeline, a special purpose vector store for document embeddings, a performant embeddings inference engine, API access to an aligned large language model, the combined functionality of which is exposed via a user interface that maintains session persistance and is secured with authN. Galileo was created to provide all of these things, integrated into a reference application. The use case of this reference application is a virtual legal research assistant, capable of answering questions against US Supreme Court decisions.

For full documentation, see https://aws-samples.github.io/aws-genai-conversational-rag-reference.

• Overview
• Mental Model
• How it Works
• Getting Started
• Security Considerations
• Contributing
• Developer Guide

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The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

There are a number of security considerations that should be taken into account prior to deploying and utilising this sample. The security section of the provided documentation outlines each of these.

If you looking to benchmark multiple LLMs and RAG engines in a simple way, you should checkout aws-samples/aws-genai-llm-chatbot. That project focuses more on experimentation with models and vector stores, while this project focuses more on building an extendable 3-tier application.