Explore the world of query embeddings and web-scale search powered by deep learning and Python in this EuroPython Conference talk. Dive into an unsupervised deep learning system built using Python and open-source libraries like Annoy and keyvi, designed to recognize similarities between queries and their vector representations. Learn how this technology improves recall for previously unseen queries and integrates into the Cliqz browser's search stack. Discover the transition from traditional keyword-based search to deep learning and NLP techniques that represent sentences and documents as fixed-dimensional vectors in high-dimensional space. Gain insights into the architecture of query embeddings, including vector indexing, approximate nearest neighbor models, and the use of Word2Vec. Explore real-world applications, latency issues in real-time search systems, and the potential for this framework to be utilized in other low-latency systems involving vector representations.