text-embedding-3-large works by encoding text into a high-dimensional vector that represents semantic meaning learned from large-scale language data. The model processes text holistically, considering word choice, order, and context, and outputs a single fixed-length vector that summarizes the meaning of the entire input.
Conceptually, the process starts with tokenization, where text is broken into subword units. These tokens are then passed through multiple neural network layers trained to capture contextual relationships. Unlike keyword-based approaches, the model understands that meaning depends on context. For example, “memory leak in service” and “process consumes increasing RAM” are treated as related concepts even though they share few exact words. The final embedding aggregates this contextual information into a vector suitable for mathematical comparison.
Once generated, these vectors are stored and queried using vector databases like Milvus or Zilliz Cloud. The model itself does not perform retrieval; it only produces representations. Milvus handles indexing and nearest-neighbor search, allowing developers to search by semantic similarity at scale. This division of responsibility makes systems easier to reason about and optimize: text-embedding-3-large focuses on meaning, while Milvus focuses on speed and scalability.
For more information, click here： https://zilliz.com/ai-models/text-embedding-3-large