Digital Strategy Assistant

This prototype explores how Large Language Models (LLMs) can enhance digital learning by providing an accessible and interactive way for the general public, educators, and administrators to engage with the Digital Strategy Assistant (DSA). Acting as a conversational guide, the chatbot allows users to ask questions and receive tailored responses aligned with DSA principles and recommendations. This approach fosters a broader understanding of technology-enhanced learning, making digital literacy concepts and strategies more approachable and relevant across educational and public contexts.

Index	Description
High Level Architecture	High level overview illustrating component interactions
Deployment	How to deploy the project
User Guide	The working solution
RAG Documentation	Documentation detailing the RAG workflows for both standard chat and course alignment discovery.
Directories	General project directory structure
API Documentation	Documentation on the API the project uses
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/
│   ├── userGuide.md
│   ├── deploymentGuide.md
│   ├── securityGuide.md
│   ├── images/
│   └── rag_infrastructure/
│       ├── course_alignment_discovery/
│       │   ├── hyperparameters.md
│       │   ├── data_ingestion/
│       │   └── text_generation/
│       └── standard_chat/
│           ├── hyperparameters.md
│           ├── data_ingestion/
│           └── text_generation/
├── frontend/
│   ├── public/
│   └── src/
│       ├── app/
│       └── components/
│           ├── chat/
│           ├── home/
│           └── ui/
└── frontendAdmin/
    ├── public/
    └── src/
        ├── app/
        └── components/
            ├── analytics/
            ├── auth/
            ├── categories/
            ├── files/
            ├── history/
            ├── prompt/
            └── ui/

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 course 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 (including the RAG worflows for both standard_chat and course_alignment_discovery)
3. /frontend: Contains the user interface of the general public application
   • /public: public assets used in the application
   • /src: contains the frontend code of the application
     • /app: Nextjs App Router
     • /components: Contains components used in the application
       • /chat: Contains chat components used in the application
       • /home: Contains home components used in the application
       • /ui: Contains shadcn ui components used in the application
4. /frontendAdmin: Contains the user interface of the administrator application
   • /public: public assets used in the application
   • /src: contains the frontend code of the application
     • /app: Nextjs App Router
     • /components: Contains components used in the application
       • /analytics: Contains chat components used in the application
       • /auth: Contains auth components used in the application
       • /categories: Contains category components used in the application
       • /files: Contains file components used in the application
       • /history: Contains chat history components used in the application
       • /prompt: Contains prompt modification components used in the application
       • /ui: Contains shadcn ui components used in the application

API Documentation

Here you can learn about the API the project uses: API Documentation.

Modification Guide

Steps to implement optional modifications such as changing the colours of the application can be found here

Changelog

N/A

Credits

This application was architected and developed by Kanish Khanna, Sean Woo, and Harshinee Sriram, with project assistance by Amy Cao. 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