Inspiration
Queens College's daily experience can be like navigating a maze of irrelevant information. Interactions with the help office staff may not always be the friendliest, often leaving you with unclear answers. Our aim is to make things better for everyone by simplifying information. We want to give web users direct access to the answers they need, cutting through the confusion and creating a more user-friendly experience for all.
What it does
Discover QCBot – your personal info guru! It holds all the website's knowledge but trims away the unnecessary. Just ask, and QCBot serves up exactly what you're looking for, making your search hassle-free and straightforward.
How we built it
We used Python flask as our back-end and HTML, CSS for the front-end. The tools that we used include Azure OpenAI API, Azure cloud to store our data, and Git/Github to manage version control and facilitate all team members to work in the project in tandem.
Challenges we ran into
We ran into several challenges. The biggest being learning how to work with Azure having no prior experience, also integrating our flask application with the azure OpenAI. Also doing web scrapping and in vectorizing the data prove to be very challenging.
Accomplishments that we're proud of
We were able to successfully deploy the AIsearch model as well as work as a team.
What we learned
We've honed our ability to distribute work efficiently by assigning tasks according to individual strengths and expertise. This tailored approach ensures that each team member operates in their best-suited field, optimizing productivity and quality. As we bring together these specialized contributions, we foster a collaborative environment where the synergy of diverse skills culminates in a cohesive and successful outcome. Effective communication and a shared understanding of overarching goals remain crucial, creating a harmonious workflow that maximizes our collective potential.
What's next for QCBot
We did not get the chance to fully deploy the website in the domain but the QCBot is functional, and we plan on adding multiple new features to train and improve the Azure OpenAI.
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