text-embedding-ada-002 works by passing input text through a trained neural network that maps language patterns into numerical representations. Internally, the model processes the text and produces a dense vector where each dimension captures some aspect of semantic meaning. The exact internal mechanics are abstracted away, but the key property is consistency: similar texts produce vectors that are close together in vector space.
From an implementation standpoint, developers interact with the model through an API. You send a string of text, and the model returns an array of floating-point numbers with a fixed length of 1536. These vectors can then be normalized or stored as-is, depending on the similarity metric you plan to use. The model is designed so that common similarity operations, such as cosine similarity, work reliably across a wide range of text lengths and domains.
These vectors are rarely used in isolation. Instead, they are typically stored in a vector database like Milvus or Zilliz Cloud, which supports efficient indexing and nearest-neighbor search. When a new query arrives, it is embedded using the same model and compared against stored vectors to find the most relevant results. This separation between embedding generation and vector search makes systems modular and scalable. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002