jina-embeddings-v2-base-en supports plain English text as input, typically provided as UTF-8 encoded strings. These strings can represent anything from short sentences and search queries to long paragraphs or full document sections. The model does not require structured formats like JSON or XML for the text itself; instead, structure is handled by the surrounding application logic. As long as the input is readable English text, the model can process it.
In real-world applications, input text often comes from diverse sources such as Markdown files, HTML pages, PDFs converted to text, or database fields. Developers usually normalize this content before embedding by stripping markup, removing boilerplate, and ensuring consistent whitespace. Although jina-embeddings-v2-base-en can technically embed formatted text, treating formatting characters as plain tokens can reduce embedding quality. Simple preprocessing generally leads to better results.
Because the model supports long sequences up to 8192 tokens, developers have flexibility in how they prepare input. They may embed entire sections or chapters instead of aggressively splitting content into small chunks. Once generated, embeddings are stored in systems like Milvus or Zilliz Cloud, where each vector can be associated with metadata describing the original format or source. The key takeaway is that jina-embeddings-v2-base-en expects clean English text, and thoughtful preprocessing improves downstream similarity search results.
For more information, click here: https://zilliz.com/ai-models/jina-embeddings-v2-base-en