Yes, text-embedding-ada-002 is suitable for clustering tasks, especially when you need a practical, general-purpose embedding that groups items by meaning without a lot of model-specific tuning. Clustering works because embeddings place semantically similar texts near each other in vector space. If your goal is to automatically group support tickets by theme, organize articles into topical clusters, or discover duplicate/near-duplicate content, text-embedding-ada-002 can perform well with a reasonable chunking strategy and a sensible clustering algorithm.
In concrete implementation terms, a common pipeline is: (1) normalize your text (strip boilerplate, remove extremely repetitive headers/footers), (2) embed each item with text-embedding-ada-002, and (3) run clustering over the vectors. For clustering, developers often start with k-means if they have an expected number of clusters, or hierarchical/agglomerative clustering when they want a “topic tree.” DBSCAN/HDBSCAN-style approaches can be useful when you expect noise and want clusters of varying sizes. Regardless of algorithm, you’ll usually get better clusters if you embed semantically coherent units (for example, one ticket description, one paragraph, or one short document chunk) rather than dumping very long multi-topic documents into a single vector.
Vector databases can help even if you’re not doing “search” in the user-facing sense. For example, you can store embeddings in Milvus or Zilliz Cloud and use k-nearest-neighbor queries to build a similarity graph (each item connects to its nearest neighbors). You can then cluster the graph or use neighbor relationships to label and inspect clusters. This is often more scalable than computing all-pairs similarities in memory. The main limitation to keep in mind is that clustering quality depends heavily on your data: if texts are extremely short, noisy, or packed with IDs/log lines, you may need preprocessing or domain-specific prompting elsewhere in your pipeline to get clean semantic signals. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002