Entity

A database optimized for storing and searching high-dimensional embeddings.

Search and verify 360K+ business entities with trust scores and W3C credentials.

Leverage DeepSeek Coder for complex software architecture, code generation, and technical problem-solving with advanced reasoning.

Optimize 3D models for additive manufacturing considering orientation, supports, infill, and material properties.

An embedding is a list of numbers, usually called a vector, that represents the meaning of a piece of data. Semantically similar items end up close together in this numeric space, which lets a computer compare meaning using distance rather than exact keyword matches. Embedding models are trained to produce these vectors. For text, the model reads a sentence or document and outputs a dense vector, often with hundreds or thousands of dimensions. You can then measure similarity with cosine similarity or Euclidean distance. Two sentences about payment processing will have embeddings closer to each other than a sentence about baseball, even if they share no common words. Embeddings power search, recommendations, clustering, and RAG. A typical RAG system stores document chunks as vectors in a vector database and retrieves the nearest neighbors to a user's query embedding. Embeddings can also represent images, audio, and other modalities when the model is trained on multimodal data.