
Worked on enhancing vector data ingestion and download workflows in the SEMOSS/Semoss repository, focusing on enabling PDF attachments as sources within the vector database. Developed the VectorAttachFileToSourceReactor to allow seamless uploading of PDFs and their association with existing CSV data, enriching the database’s analytical capabilities. Refactored the VectorFileDownloadReactor to improve handling of both single and multiple file downloads, introducing robust error handling for missing files and more reliable ZIP packaging. Leveraged Java for backend and API development, with an emphasis on file handling and vector database integration. The work improved data enrichment, search relevance, and operational reliability for analytics workflows.
May 2025: Delivered end-to-end enhancement to vector data ingestion and download workflows in SEMOSS/Semoss, enabling PDFs to be attached as sources in the vector DB and improving robustness of downloads and ZIP packaging. This work strengthens data enrichment, search relevance, and operational reliability for vector-based analytics.
May 2025: Delivered end-to-end enhancement to vector data ingestion and download workflows in SEMOSS/Semoss, enabling PDFs to be attached as sources in the vector DB and improving robustness of downloads and ZIP packaging. This work strengthens data enrichment, search relevance, and operational reliability for vector-based analytics.

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