Yes, text-embedding-3-small can handle non-English text and produce meaningful embeddings for many languages. It is trained on multilingual data, allowing it to capture semantic relationships beyond English-only use cases. This makes it useful for applications that serve global users or process multilingual content.
In practice, developers can embed text in languages such as Chinese, Spanish, French, or mixed-language inputs without changing their pipeline. Queries and documents written in the same language typically match well, and in some cases, semantically similar content across languages may also appear closer than expected. This is useful for international documentation systems, multilingual search, or region-specific user feedback analysis.
When combined with a vector database such as Milvus or Zilliz Cloud, multilingual embeddings can be stored and searched just like English ones. The database does not care about language; it only indexes vectors. Developers should still test retrieval quality per language and adjust chunking or preprocessing as needed. Overall, text-embedding-3-small provides a practical multilingual baseline without requiring language-specific models or pipelines.
For more information, click here： https://zilliz.com/ai-models/text-embedding-3-small