jina-embeddings-v2-small-en works best with clean, well-structured English text that represents a single idea or topic. Examples include sentences, short paragraphs, FAQ entries, documentation sections, and product descriptions. The model is designed to capture semantic meaning, so inputs that are clear and focused tend to produce the most useful embeddings for similarity search.
In practice, developers often preprocess text before embedding. This may include removing HTML tags, stripping Markdown formatting, normalizing whitespace, and splitting long documents into chunks. While jina-embeddings-v2-small-en can handle moderately long text, embedding extremely long or multi-topic passages can dilute semantic focus. Thoughtful chunking usually improves retrieval quality when embeddings are stored in systems like Milvus or Zilliz Cloud.
The model does not interpret formatting or structure in a special way, so tables, code blocks, or noisy text may reduce embedding quality if left unprocessed. For best results, developers should convert such content into plain English descriptions when possible. By feeding clean, focused text into jina-embeddings-v2-small-en and storing the resulting vectors in Milvus or Zilliz Cloud, developers can achieve more consistent and relevant similarity search results.
For more information, click here： https://zilliz.com/ai-models/jina-embeddings-v2-small-en