HackStack

Project Info

InsightBot

Devpost

This project did not submit a demo video on Devpost.

Inspiration

The inspiration for Insightbot came from the growing demand for personalized learning and productivity tools that can support users in managing their tasks and goals more effectively. As students, we often found ourselves struggling to balance multiple responsibilities, while searching for tailored information or assistance at critical moments. We realized that there was a need for a tool that not only helps users stay organized with to-do lists and goals tracking but also provides on-demand learning support based on their personal data, like notes or resources. This inspired us to combine the power of Retrieval-Augmented Generation (RAG) with a productivity suite to create a seamless and personalized learning experience, accessible anytime.

What it does

InsightBot is an intelligent study tool powered by RAG that delivers a personalized learning experience, helping users achieve their goals. Our platform offers multiple tools that helps users to stay organized, achieve their goals, and receive tailored support to overcome learning challenges, ensuring continuous progress at any time.

How we built it

Using NextJS, we started our project with UI/UX development. Following, we integrated an AI chatbot with Open AI API key and downstreamed the chatbot with RAG by utilizing Open AI embeddings, Pinecone database, and Pinecone API. Then, with Python, we developed backend and handled file upload, to do list, and goal tracker features.

Challenges we ran into

We encountered an issue integrating RAG into the app due to recent updates in OpenAI's embedding documentation. To resolve this, we needed to update the code to align with the latest OpenAI API for vector embeddings.

Accomplishments we're proud of

We're proud of successfully integrating RAG within the chatbot and the goal tracker. The chatbot provides personalized learning assistance based on user-uploaded documents, while the goal tracker provides information on whether the added objectives are SMART (Specific, Measurable, Achievable, Relevant, and Time-bound). Overcoming the challenge of adapting to the updated OpenAI API for embeddings was a significant accomplishment. Additionally, we developed a seamless UI/UX offering users an intuitive and productive experience.

What we learned

Throughout this project, we deepened our understanding of RAG and how to effectively integrate it with APIs like OpenAI and Pinecone. We also learned to adapt quickly to changes in documentation and APIs, improving our problem-solving abilities. On the front-end, we gained valuable experience in creating user-friendly interfaces with NextJS, while on the back-end, we sharpened our skills in database management and API integration. Additionally, collaboration taught us how to manage our time effectively and prioritize tasks under tight deadlines.

What's next

for Insightbot We plan to enhance Insightbot by allowing user to upload more than one file, and supporting more data types like videos and links. We’re also exploring how to create tasks for the to-do list automatically based on a selected SMART goal using Open AI's API.

Analysis

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Technology

Found in codeClaimed only
  • CSSIn code
  • FlaskIn code
  • LangChainIn code
  • Next.jsIn code
  • OpenAIIn code
  • PythonIn code
  • ReactIn code
  • Tailwind CSSIn code
  • TypeScriptIn code

9 of 9 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

25 KB

Source files

7

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

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