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@AI-Student-Advisor

AI Student Advisor

AI Student Advisor

A virtual companion for navigating the complexities of higher education.

Engage in insightful conversations with our conversational AI agent to receive personalized guidance on a wide array of topics—from choosing the right degree programs to selecting courses that align with your academic goals. Whether you're a prospective student exploring options or a current student seeking advice, AI Student Advisor is here to provide instant and tailored assistance.

How it works?

AI Student Advisor is a Retrieval Augmented Generation (RAG) based conversational AI agent. Essentially, it a chat application with two primary components: retrieval and generation. The retrieval component is responsible for finding the most relevant information from a large corpus of documents, which we call the context. The generation component is then responsible for interacting with an external LLM (Large Language Model) to generate a response by providing it with the user query and the retrieved context. The generated response is then returned to the user.

Retrieval Augmented Generation

source: [LlamaIndex - Retrieval Augmented Generation](https://docs.llamaindex.ai/en/stable/getting_started/concepts.html#retrieval-augmented-generation-rag)

Development

Phase 1: Idea Verification - Basic Prototype

Goals

  • Front-end: Setup basic AI chat application - React + TypeScript
  • Back-end - API architecture and basic setup - NodeJS + Express
  • AI - Setup basic RAG chat engine on selected uOttawa webpages - LangChain

Roles

  • Front-end: Xiaoxuan
  • Back-end: Victor
  • AI: Pranav

Timeline

Expected completion date: January 31, 2024

Phase 2: Functional Prototype + Responsive Design

Goals

  • Front-end: Complete front-end responsive web design and features
  • Back-end: Code all endpoints, setup authentication or authorization, testing
  • AI: Setup hosted vector database, setup complete LangChain pipeline, Testing

Roles

  • Front-end:
  • Back-end:
  • AI:

Timeline

Expected completion date: March 1, 2024

Phase 3: Beta Release

Goals

  • Front-end: Complete any remaining design enhancements (example, based on user feedback), documentation
  • Back-end: Implement any additional features required (monitoring etc.), documentation, testing
  • AI: Testing and improvements, documentation

Roles

  • Front-end:
  • Back-end:
  • AI:

Timeline

Expected completion date: March 31, 2024

Contributors

This project was built as part of Honours Project (CSI4911) at the University of Ottawa.

The project was graciously supervised by Dr. Hussein Al Osman.

The following students contributed to the project:

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