This tutorial demonstrates building a Personal Health Knowledge Base using retrieval-augmented generation (RAG). The goal is to transform static medical PDF reports into a dynamic, queryable system. LlamaIndex orchestrates the process, Unstructured.io handles complex PDF data extraction, and Pinecone serves as the vector store. This system combines historical personal data with current medical literature.The architecture employs a hybrid intelligence approach. DuckDB is used for structured SQL-based trend analysis of personal metrics, while Pinecone stores unstructured semantic context from medical research. Unstructured.io's hi_res strategy is crucial for extracting tables from PDFs.The system requires Python 3.10+, Unstructured.io, and Pinecone API keys. Tables are extracted using Unstructured.io and filtered for structured analysis. Structured data like biomarker levels are stored in DuckDB for time-series analysis.Medical notes and research are stored in Pinecone for semantic retrieval. LlamaIndex's SQLAutoVectorQueryEngine intelligently routes user queries. It directs questions about personal trends to DuckDB and questions about medical implications to Pinecone.This allows for comprehensive queries, such as analyzing personal cholesterol trends against current guidelines. The final output provides actionable health insights by merging personal trend analysis with evidence-based context. While this setup is for learning, production systems need stricter data privacy and medical grounding. The conclusion emphasizes that RAG can contextualize data for actionable insights, moving beyond simple document chatting.