Developed a targeted enhancement for the Borye/ragflow repository by integrating document IDs into knowledge base file entries, addressing the need for improved context, traceability, and searchability within the system. Leveraging Python for backend and API development, the solution focused on database management to ensure each file entry could be reliably associated with its source document. This approach facilitated faster issue diagnosis and supported auditability by aligning changes with tracked issues. The work demonstrated a methodical application of backend engineering principles, resulting in more robust information retrieval and streamlined knowledge base maintenance, while maintaining clear alignment with project requirements and workflows.