text-embedding-3-large produces a fixed-length numerical vector that represents the semantic meaning of the input text. This vector consists of floating-point numbers and has the same dimensionality for every input, regardless of whether the text is short or long. The output is not human-readable; it is designed for mathematical comparison using similarity metrics.
From an implementation standpoint, the output is typically an array of floats, such as [0.0123, -0.451, 0.882, ...]. Each position in the vector contributes to representing meaning, but no single dimension has a clear or interpretable label. What matters is how vectors relate to one another. Texts with similar meanings produce vectors that are close together, while unrelated texts are farther apart. For example, two paragraphs describing different database indexing strategies will produce vectors that are closer to each other than to a paragraph about user authentication.
These vectors are almost always consumed by a vector database such as Milvus or Zilliz Cloud. In Milvus, you store the vector in a FLOAT_VECTOR field and associate it with metadata like document ID, language, or source. The database then uses this output to perform similarity searches using cosine similarity or inner product. The model’s output is therefore best understood as a semantic coordinate, not a standalone artifact, and its real value emerges when indexed and queried at scale.
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