Conceptually, text-embedding-3-small works by mapping text into a dense numerical vector space where semantic similarity becomes measurable distance. Instead of relying on exact word matches, the model learns patterns of meaning from large amounts of text and encodes those patterns into vectors. Two pieces of text with similar meaning will produce vectors that are close together, even if they use different words or sentence structures.
At a high level, the process starts with tokenization, where text is broken into subword units. These tokens are processed by a neural network trained to understand context, meaning, and relationships between words. The model does not simply count words; it considers word order and surrounding context. For example, “server timeout error” and “request failed due to latency” produce embeddings that are close, because the underlying meaning is similar. The final output is a fixed-length vector that represents the entire input text.
Once generated, these vectors are not useful on their own; they become powerful when stored and queried efficiently. This is where vector databases such as Milvus and Zilliz Cloud fit naturally. Milvus indexes embeddings and allows fast similarity searches across large datasets. Conceptually, text-embedding-3-small handles understanding text, while Milvus handles finding the closest vectors at scale. This clean separation helps developers design systems that are easier to reason about and maintain.
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