Yes, text-embedding-ada-002 can be used for classification, especially when you want a simple, effective baseline without training a dedicated classifier from scratch. The typical approach is to treat classification as a similarity problem: embed the input text, embed examples or label descriptions, and pick the closest match. This can work well for intent routing (“billing issue” vs “technical issue”), topic tagging (“security” vs “performance”), or lightweight moderation categories, provided your labels and examples are well-chosen.
There are two common implementation patterns. The first is “prototype” classification: you maintain a small set of representative examples per class (or a class description), embed them once, and then classify new items by nearest-neighbor similarity. If you have 20 intents, you might store 5–20 example embeddings per intent and compute similarities to vote on the best label. The second pattern is supervised learning on embeddings: generate embeddings for labeled training data, then train a simple model (logistic regression, linear SVM, or shallow neural net) on top of the vectors. This often performs surprisingly well, and it’s easy to iterate because you can keep your feature extraction fixed (the embedding model) while improving labels and training.
When datasets get large, storing embeddings and running similarity-based classification can benefit from a vector database such as Milvus or Zilliz Cloud. For example, you can store labeled examples and retrieve the top-k nearest labeled neighbors for a new item, then use majority vote or similarity-weighted vote for the final class. This makes classification scalable and also gives you “explanations” in the form of nearest examples. The main caveat is that classification quality depends on label clarity and data hygiene: if classes overlap heavily or text is dominated by templates/boilerplate, you’ll want preprocessing and careful example selection to avoid drifting into “looks similar but wrong label.” For more information, click here：https://zilliz.com/ai-models/text-embedding-ada-002