Glossary Relationship Mapping and Report Builder for Banking Terms

Added Nov 2025 3 design docs

A banking glossary lists hundreds of terms alphabetically, but real understanding lives in the relationships: collateral relates to loan-to-value, which relates to credit risk, which connects to capital adequacy. Making those connections explicit turns a flat word list into a map a learner or analyst can actually navigate. The intern builds a python tool that accepts a set of banking terms or an uploaded glossary and produces a structured relationship report. A langgraph workflow manages the stages: terms are parsed and normalized, candidate relationships are identified, and each connection is explained. An agentic-ai layer coordinates the process, deciding which term pairs deserve deeper treatment, while generative AI through openai models writes beginner-friendly explanations of how each pair of concepts relates, whether by definition, dependency, or contrast. The final output is an organized report mapping the terminology landscape, suitable for onboarding materials or study guides. The project teaches terminology modeling, systematic AI-generated explanation, and modular workflow design, and gives the intern a tangible artifact showing they can turn unstructured domain vocabulary into structured, useful knowledge.

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