Visual Explorer for Related Banking Terms and Definitions

Added Nov 2025 3 design docs

Learning banking vocabulary term by term is like memorizing a city street by street without a map: each definition makes sense alone, but the geography never forms. A tool that starts from any term and shows what surrounds it, with clear explanations of each connection, gives learners that missing map. The intern builds the explorer as a streamlit application in python. Banking terms and their definitions are embedded into a vector database, so semantically related concepts sit near each other and can be retrieved for any starting term. When a user enters a term such as collateral, a retrieval-augmented generation pipeline finds the most closely related terms, and generative AI writes a short explanation of each connection, describing, for instance, how collateral relates to secured lending and loan-to-value ratios. The interface presents the term, its neighbors, and the explanations together, and users can click through to any neighbor to continue exploring the vocabulary graph. The project is a friendly introduction to embeddings and RAG: the intern learns how semantic similarity is computed, how retrieval feeds generation, and how to shape those pieces into an educational exploration experience.

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