text-embedding-ada-002 embeddings should be stored as dense numerical vectors in a system optimized for similarity search rather than in traditional relational tables. Each embedding is a 1536-dimensional floating-point vector, and storing it efficiently is important for both performance and scalability. While it is technically possible to store embeddings as raw arrays in general-purpose databases, this approach quickly becomes inefficient as data size grows.
In most production systems, embeddings are stored alongside metadata such as document IDs, titles, timestamps, or access control fields. This metadata allows filtering and contextual retrieval in addition to pure similarity search. For example, you might store an embedding for each document chunk along with its document ID and language. At query time, you can search for similar vectors while also filtering by metadata constraints. This pattern keeps retrieval both relevant and controllable.
Vector databases such as Milvus or Zilliz Cloud are built specifically for this purpose. They provide indexing structures optimized for dense vectors, support common similarity metrics, and scale to millions or billions of embeddings. By storing text-embedding-ada-002 vectors in such a system, developers avoid reinventing low-level indexing logic and can focus on application behavior. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002