Interactive Knowledge Graph Explorer for Banking Documents

Added Nov 2025 3 design docs

Banks accumulate enormous libraries of reports, contracts, and statements, and the connections buried across those documents, such as which policies reference which entities, or how terms in one contract relate to clauses in another, are nearly impossible to see by reading files one at a time. Analysts and compliance staff need a way to explore that knowledge as a connected map rather than a pile of PDFs. The intern builds an exploration suite in python with a streamlit front end where users upload diverse banking documents. Text is chunked and embedded into a vector database, and a retrieval-augmented generation pipeline finds related passages across the corpus while generative AI produces explanations of how concepts, entities, and policies are linked. The centerpiece is an interactive map of key terms and their relationships that users can query in natural language, asking, for example, how a policy connects to a counterparty mentioned in a different report, with RAG grounding every answer in the source documents. The project is deliberately modular so a team can divide ingestion, retrieval, and interface work, and it leaves the intern with a portfolio-quality demonstration of embeddings, semantic retrieval, and grounded generation applied to a hard, realistic problem.

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