Build a fast, offline image similarity search engine in Java using Quarkus, ONNX Runtime, LangChain4j, and PostgreSQL with pgvector. This hands-on tutorial shows how to generate CLIP embeddings locally, store them in pgvector, and perform real-time visual search—without cloud APIs, latency, or per-request costs. Ideal for enterprise Java developers building AI-powered features at scale.
Hey Markus, I am following you for some time now and I very much appreciate the ideas you come up with and the level of details you explain and show how the ideas are implemented. Keep up the good work. I wish you all the best with your publication.
Just to give people a feel for it generally. I’m personally not aware of specific benchmark numbers. Only the general cognitive research about wait times. Thanks for reading and asking!
Hey Markus, I am following you for some time now and I very much appreciate the ideas you come up with and the level of details you explain and show how the ideas are implemented. Keep up the good work. I wish you all the best with your publication.
Best Michael
Thank you Michael 🙏
Very interesting Article, Markus
Is Latency: 20–50 ms the benchmark?
Just to give people a feel for it generally. I’m personally not aware of specific benchmark numbers. Only the general cognitive research about wait times. Thanks for reading and asking!
Okay thanks