AI / RAG
AI Medical Report Summarizer
A RAG system that analyzes and summarizes complex medical reports with FastAPI, LangChain, Qdrant, and a TypeScript client — fully Dockerized.
Overview
An intelligent Retrieval-Augmented Generation system designed to analyze and summarize complex medical reports. Documents are embedded into Qdrant, retrieved semantically, and summarized through a LangChain-orchestrated pipeline so critical health insights stay grounded in the source report.
The Problem
Medical reports are dense and jargon-heavy. Clinicians and patients need fast, accurate summaries that stay faithful to the source document rather than free-form LLM hallucinations.
Architecture
Key Decisions
RAG over raw summarization
Summaries are grounded in retrieved chunks from the uploaded report, reducing hallucination risk on high-stakes content.
Qdrant for medical embeddings
Vector retrieval finds clinically relevant sections even when wording differs between the question and the report.
LangChain orchestration
LangChain connects ingestion, retrieval, and LLM calls into a maintainable pipeline behind FastAPI.
Docker Compose delivery
Backend, client, and dependencies ship as a multi-container stack for reproducible local demos.
Failure Modes
Ungrounded medical claims
RAG retrieves report chunks before generation so answers stay tied to uploaded content.
Missed clinically relevant sections
Semantic search over Qdrant surfaces relevant passages even when terminology differs.
Environment drift
Docker Compose pins the FastAPI + Qdrant + client stack for consistent runs.
Implementation
- FastAPI async REST API for document ingestion, processing, and querying
- LangChain RAG pipeline over medical report embeddings
- Qdrant vector store for semantic retrieval
- TypeScript / Node.js client for upload and summary viewing
- Dockerfile + docker-compose multi-container orchestration
Lessons
- —For medical content, grounding via RAG is non-negotiable — raw LLM summaries are not enough.
- —A thin FastAPI + LangChain layer keeps the retrieval path auditable.
- —Shipping the stack in Docker makes demos and handoffs reliable.