text-embedding-ada-002 produces a fixed-length numerical vector that represents the semantic meaning of the input text. Specifically, the output is an array of floating-point numbers with 1536 dimensions. Each number on its own has no direct human-readable meaning, but together they form a vector that can be compared mathematically with other vectors to measure semantic similarity between texts.
From an implementation standpoint, developers typically receive this output as a JSON array from the embedding API. The vector can be stored as-is or normalized, depending on the similarity metric being used. Common operations include cosine similarity or inner product, both of which work well with this type of dense vector. For example, if you embed two sentences like “reset account password” and “forgot my login credentials,” their vectors will be close together in vector space even though they share few exact words.
In production systems, these vectors are rarely used in isolation. Instead, they are stored in a vector database such as Milvus or Zilliz Cloud, which are optimized for indexing and searching large collections of vectors efficiently. A typical workflow involves embedding documents once, storing the vectors, and embedding incoming queries at runtime to perform similarity search. This makes it possible to retrieve relevant content quickly and reliably at scale. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002