Project Info

Omnis

Inspiration

As students ourselves who have taken a breadth of classes, we realize that each class uses its own organization system and platforms. Some classes upload materials in three different places with lecture videos and supplemental videos in another place, whereas other classes keep everything in one large folder. Being able to track how each class operates is difficult, and we dont want that to impede a student's learning process. We wanted to create a smarter way to study—one that goes beyond static notes and videos. Keeping track of lecture materials and truly understanding concepts can be overwhelming, so we built Omnis to act as an interactive AI tutor, making learning more intuitive, organized, and personalized.

What it does

Our tool allows you to keep everything in one place and also uses LLMs to parse this information so you can actually interact with it! Omnis allows students to upload lecture slides, notes, videos, and homework into a single system where they can chat with an AI assistant trained on their own materials. Users can ask questions, get clarifications, and explore concepts with references directly from their coursework, creating a personalized and centralized learning experience.

How we built it

We used InterSystems' IRIS Vector Search to embed the documents and information so that users can search and interact with it. We used ReactJS for our frontend, we run our own server with Python Flask, and we use Firebase for Cloud Storage and Authentication. We use OpenAI's language models for reasoning and generation. We built a pipeline to extract information from PDFs, videos, and notes into text embeddings, thus allowing the AI to generate responses grounded in user-provided content. A chat interface lets students interact seamlessly with the AI for real-time assistance. We also provide references back to the original documents that the user uploaded, specific to the timestamp and sentence so that the user can see the contextual grounding.

Challenges we ran into

Chunking and Context limits. Ensuring AI-generated responses remain grounded in provided materials and don’t hallucinate.

Accomplishments we're proud of

Increasing generality of our tool with different file types and platform integrations. Implementing a robust retrieval system that provides accurate, source-backed answers. Synthesizing all sources into a knowledge base and a clean and easy-to-use AI!

What we learned

Utilizing IRIS vector search Learned about new lecture content through Omnis!

What's next

Integrating more streams for class content, then deploying for students to use!

Analysis

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Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only

CSSIn code
FirebaseIn code
HTMLIn code
JavaScriptIn code
OpenAIIn code
PythonIn code
ReactIn code

7 of 7 appear in the indexed code.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

90 KB

Source files

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.