Build Local Image Search with Quarkus, ONNX, and pgvector is a tutorial demonstrating how to create an offline image similarity search engine using Java's Quarkus framework, ONNX Runtime for local CLIP model inference, and PostgreSQL's pgvector extension for vector storage.

The approach eliminates cloud API dependencies, latency, and per-request costs by running visual search entirely within a Java microservice. The tutorial covers exporting CLIP vision models to ONNX format, implementing custom embedding models in LangChain4j, storing embeddings in pgvector, and exposing REST endpoints for real-time image matching in enterprise environments.