Building a Personal Medical Records RAG Pipeline with Qdrant and Local Embeddings

Short summary

This tutorial walks through building a personal health knowledge base using a RAG pipeline with Qdrant, Unstructured.io, and Sentence-Transformers. It covers parsing messy medical PDFs (lab results, scans, EMRs) into embeddings, storing them in a local vector database, and querying semantically across years of records. The pipeline uses local models to keep sensitive health data private, with a FastAPI interface for retrieval and LLM synthesis.

• •RAG pipeline turns 10 years of scattered medical records into a searchable vector database
• •Unstructured.io handles complex medical PDF parsing with layout-aware OCR
• •Local embeddings via Sentence-Transformers keep sensitive health data private

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