text-embedding-ada-002 supports a wide range of natural languages, making it suitable for multilingual applications. It is designed to generate meaningful embeddings for many commonly used languages, including English and other major world languages, without requiring language-specific configuration. This allows developers to use a single model across international products or datasets.
In practice, this means you can embed documents or queries written in different languages and still perform semantic search or clustering within the same system. For example, a multilingual help center might store articles in several languages and embed them using the same model. While cross-language similarity may not be perfect in all cases, text-embedding-ada-002 generally captures enough semantic structure to be useful for many global applications.
When working with multilingual data at scale, embeddings are often stored in a vector database such as Milvus or Zilliz Cloud. These systems can store vectors regardless of language and apply the same similarity search logic across the entire dataset. Developers can also add metadata filters, such as language codes, to refine search results when needed. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002