Developers Build AI System to Query Personal Medical Records Using RAG and Vector Search

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A tutorial published on DEV Community outlines how to build a personal Electronic Health Record (EHR) system using Retrieval-Augmented Generation (RAG) technology. The project addresses a common problem: medical documents are unstructured, scan-heavy, and difficult to search through manually. The solution combines Unstructured.io for intelligent PDF parsing and OCR, BGE-M3 embeddings for medical semantic accuracy, and Milvus as a vector database for scalable storage. Users can query their medical history in natural language — for example, asking about glucose trends over multiple years — with responses synthesized via LlamaIndex and a large language model. The system is designed to run locally via Docker or on Zilliz Cloud, requiring Python libraries such as pymilvus, llama-index, and sentence-transformers.

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