The vector size of text-embedding-ada-002 is 1536 dimensions. This means that every piece of text passed to the model is converted into a vector containing exactly 1536 floating-point values. This fixed size is important because it allows all embeddings to be compared using the same mathematical operations.
From a technical perspective, 1536 dimensions represent a balance between expressiveness and efficiency. The vector is large enough to encode meaningful semantic detail but small enough to store and search efficiently in most systems. Developers need to account for this size when planning storage and memory usage, especially when embedding millions of documents.
Vector databases such as Milvus or Zilliz Cloud are well suited for handling vectors of this dimensionality. They support indexing methods optimized for dense vectors and can scale horizontally as data grows. Knowing the vector size upfront makes capacity planning and system design more predictable. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002