From Dust-Covered PDFs to AI-Powered Insights: Building a 10-Year Health RAG Pipeline

A developer built a personal health knowledge base that uses retrieval-augmented generation to turn a decade of medical checkup PDFs into a queryable system. The pipeline combines LlamaIndex for orchestration, Unstructured.io for OCR and table extraction, DuckDB for structured SQL trend analysis, and Pinecone as the vector store, routing queries between historical biomarker data and medical literature via LlamaIndex's SQLAutoVectorQueryEngine.

We’ve all been there: staring at a decade's worth of medical checkup PDFs, trying to remember if that cholesterol spike in 2018 was a fluke or a trend. Manual tracking is tedious, and generic medical advice lacks your personal context. In this tutorial, we are building a sophisticated Personal Health Knowledge Base using RAG Retrieval-Augmented Generation . We will transform static PDF reports into a dynamic, queryable system that combines your historical data with the latest medical literature. By leveraging LlamaIndex for orchestration, Unstructured.io for complex OCR, and Pinecone as our high-performance vector store, we’ll move beyond simple chatbots into the realm of Health Data Engineering . Building a health assistant isn't just about semantic search; it's about trend analysis . To achieve this, we use a hybrid approach: DuckDB for structured SQL-based trend analysis e.g., "Show me my fasting blood sugar over 10 years" and Pinecone for unstructured semantic retrieval of medical context. php graph TD A PDF Health Reports -- B Unstructured.io B -- C{Data Router} C -- |Structured Metrics| D DuckDB - SQL Store C -- |Semantic Context| E Pinecone - Vector Store F Medical Research APIs -- E G User Query: Is my LDL trend dangerous? -- H LlamaIndex Orchestrator H -- D H -- E D -- I Personal Trend Analysis E -- J Evidence-Based Context I & J -- K Final Health Insight To follow along, you'll need: LlamaIndex , DuckDB , Pinecone , OpenAI Medical PDFs are notorious for their nested tables and weird formatting. Standard PDF readers often fail. We'll use Unstructured.io to extract clean, structured elements. python from unstructured.partition.pdf import partition pdf Partitioning the PDF into structured elements elements = partition pdf filename="report 2023.pdf", strategy="hi res", Extracts tables with high fidelity infer table structure=True, chunking strategy="by title", Filtering tables for our DuckDB trend analysis tables = el for el in elements if el.category == "Table" print f"Extracted {len tables } tables from your health report." We need to store the raw numbers like Glucose levels in DuckDB for time-series analysis and the medical notes/research in Pinecone for RAG. python import duckdb from llama index.vector stores.pinecone import PineconeVectorStore from llama index.core import StorageContext, VectorStoreIndex 1. Setup Structured Store DuckDB con = duckdb.connect "health history.db" con.execute "CREATE TABLE IF NOT EXISTS biomarkers date DATE, marker TEXT, value FLOAT, unit TEXT " 2. Setup Vector Store Pinecone import pinecone pc = pinecone.Pinecone api key="YOUR API KEY" pinecone index = pc.Index "health-rag" vector store = PineconeVectorStore pinecone index=pinecone index storage context = StorageContext.from defaults vector store=vector store The magic happens when we ask a question that requires both historical data and external medical knowledge. We use LlamaIndex’s SQLAutoVectorQueryEngine to route queries intelligently. python from llama index.core.query engine import SQLAutoVectorQueryEngine Defining the tool logic If the user asks 'How has my HbA1c changed?', the engine queries DuckDB. If the user asks 'What are the implications of high Ferritin?', it queries Pinecone. query engine = SQLAutoVectorQueryEngine sql query engine=sql engine, vector query engine=vector engine, description="Useful for answering questions about health trends and medical research." response = query engine.query "Analyze my LDL cholesterol levels over the last 10 years and cross-reference " "with the latest AHA guidelines on cardiovascular risk." print f"🚀 Insight: {response}" While this setup works for a local "Learning in Public" project, production-grade health platforms require much stricter data privacy HIPAA compliance and more robust medical grounding. For advanced patterns on handling sensitive medical data, optimizing RAG latency, and building production-ready health pipelines, I highly recommend checking out the WellAlly Tech Blog https://www.wellally.tech/blog . They have incredible deep dives on how to bridge the gap between AI research and clinical-grade applications. By combining Unstructured.io for data extraction, DuckDB for structured trends, and Pinecone for semantic search, we've turned a pile of useless paper into a life-saving knowledge base. RAG is not just about "chatting with docs"—it's about contextualizing data to provide actionable insights. 🩺 What are you building with RAG? Drop a comment below or share your latest project. Let's learn in public 🚀