You’re looking at a satellite image. Green, trees, maybe a river. Forest? Farmland? Wetlands? Without knowing where this is, even a human would struggle. A pine forest in Sweden and a eucalyptus plantation in Brazil look nearly identical from 700 km up…
Executive Summary: Embedding dimension — the length of the vector used to represent text, images or other data — is a critical hyperparameter for semantic search. Higher dimensions let embeddings capture finer-grained semantics, often boosting recall a…
credits: ESAFor decades, geospatial analysis has relied on pixel-based methods. Techniques like template matching, spectral indices, and hand-crafted features work well for narrow, well-defined problems. But as satellite and aerial imagery volumes have…
The surprisingly elegant math that lets computers understand that “dog” and “puppy” are related — and why this powers everything from…Continue reading on Medium »
Visualizing Semantic Search: How AI Understands Meaning Using Vector EmbeddingsContinue reading on Medium »
One Embedding model to rule them allContinue reading on Towards AI »
What actually happens when text becomes numbers, how a vector database searches by meaning, and how RAG ties it all together.Continue reading on Medium »
Continue reading on Towards AI »
Many ONNX models exceed the 2GB protobuf limit and store weights in external data files. Vespa now supports these models for embedders.
Documents are embedded once — worth the spend for maximum quality. Queries hit you on every request. This is what drives your cost at scale. Asymmetric retrieval with Voyage AI and Vespa. Real numbers, real config.