jina-embeddings-v2-base-en works best with clean, well-structured English text that focuses on a single topic or idea. Typical examples include sentences, short paragraphs, FAQ entries, documentation sections, and product descriptions. These kinds of inputs allow the model to produce embeddings that clearly represent semantic intent, which leads to better similarity search results.
In real applications, developers usually preprocess text before embedding. This often involves stripping HTML or Markdown formatting, removing navigation boilerplate, normalizing whitespace, and splitting long documents into logical sections. Although jina-embeddings-v2-base-en supports long inputs up to 8192 tokens, embedding very long, multi-topic text can blur semantic focus. Thoughtful chunking generally improves retrieval accuracy when embeddings are stored in a vector database such as Milvus or Zilliz Cloud.
The model treats all input as plain text and does not interpret structure like tables or code blocks in a special way. If such content is important, developers often convert it into descriptive text or store it separately with metadata. By feeding clean, focused English text into jina-embeddings-v2-base-en and managing embeddings in Milvus or Zilliz Cloud, developers can achieve more consistent and relevant semantic search behavior across a wide range of applications.
For more information, click here: https://zilliz.com/ai-models/jina-embeddings-v2-base-en