FAISS-Based Vector Embeddings of Clinical Diabetes Dataset for LLM-Driven Analytical Systems

This dataset provides a FAISS-based vector database constructed from a structured clinical diabetes dataset to support advanced Artificial Intelligence (AI) and Large Language Model (LLM)-driven analytical workflows. The original tabular patient records, including demographic and clinical attributes such as age, gender, body mass index (BMI), HbA1c levels, blood glucose levels, and comorbid conditions, were transformed into structured textual representations to enable semantic processing.These textual records were embedded using the all-MiniLM-L6-v2 sentence transformer model, generating dense vector representations suitable for similarity search and retrieval tasks. The embeddings were indexed using the FAISS (Facebook AI Similarity Search) library, enabling efficient large-scale vector retrieval for Retrieval-Augmented Generation (RAG) pipelines and agent-based reasoning systems.The dataset is specifically designed to support reproducible research in:Retrieval-Augmented Generation (RAG)Agentic AI systems with tool executionClinical decision support simulationsSemantic search over structured healthcare dataPerformance evaluation of LLM-driven analytical agentsAdditionally, the dataset supports experimental analysis of system-level characteristics such as latency, scalability, and query-response behavior when integrated with LLM frameworks. It is suitable for benchmarking, prototyping, and evaluating AI systems that operate on structured clinical data through semantic abstraction.This resource enables interdisciplinary research at the intersection of artificial intelligence, data science, and healthcare analytics, facilitating the development of intelligent systems for clinical insight generation and data-driven decision-making.