During October 2025, N.C. contributed to the BU-Spark/ml-bpl-rag repository by enhancing document embedding functionality and updating the underlying architecture. They implemented metadata-enriched embeddings, allowing both text and associated metadata to be stored, which improved the informativeness of the embeddings for downstream machine learning tasks. To address PostgreSQL compatibility, N.C. introduced a solution that serializes metadata to JSON before batch appends, reducing runtime errors and import issues. The migration away from a legacy Python script to a new embedding handling architecture streamlined maintenance and future upgrades. Their work demonstrated proficiency in Python scripting, data processing, and database management.