text-embedding-ada-002 solves problems where understanding the meaning of text is more important than matching exact words. Traditional text systems often fail when users phrase the same idea differently, leading to missed results or poor relevance. By representing text as vectors that capture semantic relationships, this model enables systems to recognize related content even when wording varies significantly.
In practical applications, this capability is useful for semantic search, document retrieval, clustering, and basic classification. For example, a knowledge base search feature can return relevant articles even if the user’s query does not share keywords with the stored content. Similarly, embeddings can be used to group customer support tickets by underlying issue or to detect duplicate or near-duplicate documents in large datasets. These tasks are difficult to implement reliably using keyword-based approaches alone.
At scale, these problems are typically addressed by combining embeddings with a vector database such as Milvus or Zilliz Cloud. The embedding model provides the semantic signal, while the vector database handles indexing, filtering, and efficient similarity search. This architecture allows developers to build systems that remain accurate and performant as data volume grows. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002