Quantum AI

This prototype addresses the challenges in accessing comprehensive learning materials in the field of quantum matter by leveraging Large Language Models (LLMs) to create a user-friendly platform for users at the Stewart Blusson Quantum Matter Institute. The solution will consolidate research on quantum materials into a centralized, interactive database and incorporate generative AI to enhance engagement and accessibility.

Index	Description
High Level Architecture	High level overview illustrating component interactions
Deployment	How to deploy the project
User Guide	The working solution
Directories	General project directory structure
RAG Documentation	Documentation on how the project uses RAG
Changelog	Any changes post publish
Credits	Meet the team behind the solution
License	License details

High-Level Architecture

The following architecture diagram illustrates the various AWS components utilized to deliver the solution. For an in-depth explanation of the frontend and backend stacks, please look at the Architecture Guide.

Deployment Guide

To deploy this solution, please follow the steps laid out in the Deployment Guide

User Guide

Please refer to the Web App User Guide for instructions on navigating the web app interface.

Directories

├── cdk
│   ├── bin
│   ├── data_ingestion
│   ├── lambda
│   ├── layers
│   ├── lib
│   ├── text_generation
├── docs
└── frontend
    ├── public
    └── src
        ├── assets
        ├── components
        ├── functions
        └── pages
            ├── admin
            └── user

1. /cdk: Contains the deployment code for the app's AWS infrastructure
   • /bin: Contains the instantiation of CDK stack
   • /data_ingestion: Contains the code required for the Data Ingestion step in retrieval-augmented generation. This folder is used by a Lambda function that runs a container which updates the vectorstore for a topic when files are uploaded or deleted.
   • /lambda: Contains the lambda functions for the project
   • /layers: Contains the required layers for lambda functions
   • /lib: Contains the deployment code for all infrastructure stacks
   • /text_generation: Contains the code required for the Text Generation step in retrieval-augmented generation. This folder is used by a Lambda function that runs a container which retrieves specific documents and invokes the LLM to generate appropriate responses during a conversation.
2. /docs: Contains documentation for the application
3. /frontend: Contains the user interface of the application
   • /public: public assets used in the application
   • /src: contains the frontend code of the application
     • /assets: Contains assets used in the application
     • /components: Contains components used in the application
     • /functions: Contains utility functions used in the application
     • /pages: Contains pages used in the application
       • /admin: Contains admin pages used in the application
       • /user: Contains user pages used in the application

RAG Documentation

Here you can learn about how this project performs retrieval-augmented generation (RAG). For a deeper dive into how we use Large Language Models (LLMs) to generate text, please refer to the Text Generation folder. For more knowledge on how data is consumed and interpreted for the LLM, please refer to the Data Ingestion folder.

Changelog

N/A

Credits

This application was architected and developed by Abhi Verma and Arshia Moghimi. Thanks to the UBC Cloud Innovation Centre Technical and Project Management teams for their guidance and support.

License

This project is distributed under the MIT License.

Licenses of libraries and tools used by the system are listed below:

PostgreSQL license

• For PostgreSQL and pgvector
• "a liberal Open Source license, similar to the BSD or MIT licenses."

LLaMa 3 Community License Agreement

• For Llama 3 70B Instruct model