text-embedding-3-small can embed any text-based data, as long as it can be represented as a string. This includes natural language documents, short queries, titles, logs, user feedback, code comments, and structured text fields such as product descriptions or FAQ entries. The model is flexible and does not require a specific schema or format.
In practice, developers often embed chunks of text rather than entire documents. For example, long documents are split into paragraphs or sections before embedding, which improves retrieval accuracy. Short texts like search queries or chat messages also work well, even if they are incomplete sentences. Because the model captures semantic meaning, it can handle variations in tone, grammar, and phrasing. This makes it suitable for messy real-world data such as support tickets or internal notes.
Once embedded, this data is typically stored in a vector database like Milvus or Zilliz Cloud. Milvus does not care whether the original data was a paragraph, a sentence, or a log line; it only operates on vectors. As long as you maintain a mapping between vectors and source records, you can embed almost any text-based dataset and make it searchable or comparable using text-embedding-3-small.
For more information, click here： https://zilliz.com/ai-models/text-embedding-3-small