Vectorize Medical Procedures for Semantic Search with TUSS, Gemini & pgVector

Description

This workflow vectorizes the TUSS (Terminologia Unificada da Saúde Suplementar) table by transforming medical procedures into vector embeddings ready for semantic search.

It automates the import of TUSS data, performs text preprocessing, and uses Google Gemini to generate vector embeddings. The resulting vectors can be stored in a vector database, such as PostgreSQL with pgvector, enabling efficient semantic queries across healthcare data.

What Problem Does This Solve? Searching for medical procedures using traditional keyword matching is often imprecise. This workflow enhances the search experience by enabling semantic similarity search, which can retrieve more relevant results based on the meaning of the query instead of exact word matches.

How It Works

1. Import TUSS data: Load medical procedure entries from the TUSS table. 2. Preprocess text: Clean and prepare the text for embedding. 3. Generate embeddings: Use Google Gemini to convert each procedure into a semantic vector. 4. Store vectors: Save the output in a PostgreSQL database with the pgvector extension.

Prerequisites

1. An n8n instance (self-hosted). 2. A PostgreSQL database with the pgvector extension enabled. 3. Access to the Google Gemini API. 4. TUSS data in a structured format (CSV, database, or API source).