Clinical nutrition studies generate diverse research materials, including study protocols, case report forms, dietary records, laboratory measurements, body composition data, and final reports. However, these resources are often fragmented across storage systems and remain underutilized after study completion. Large language models (LLMs) offer opportunities for knowledge retrieval and documentation, but cloud-based services raise concerns regarding data privacy, institutional governance, and regulatory compliance. This Research Note proposes an institutional local LLM-based framework for secure knowledge retrieval, research documentation, and knowledge reuse in clinical nutrition research. The framework integrates multidisciplinary perspectives from clinical nutrition, human intervention trials, and artificial intelligence. Its architecture comprises five interconnected layers: a clinical nutrition data layer encompassing research documents and structured datasets; a document processing layer for text extraction, metadata generation, and standardization; a knowledge repository layer combining structured and vector databases for metadata management and semantic retrieval; a local LLM layer integrating retrieval-augmented generation with a separate numerical analysis pipeline; and a researcher interface supporting natural language queries and evidence-linked documentation. The proposed framework provides a conceptual foundation for secure, reusable institutional research knowledge management.