# What limitations should developers understand before using jina-embeddings-v2-small-en? Source: https://zilliz.com/ai-faq/what-limitations-should-developers-understand-before-using-jinaembeddingsv2smallen Author: zilliz The most important limitation is scope: jina-embeddings-v2-small-en is English-only, so it is not a safe default for multilingual datasets. If your corpus includes Japanese, Chinese, Spanish, or mixed-language content, embeddings may become noisy and similarity search quality will degrade. In a real system, this often shows up as confusing retrieval results where unrelated documents cluster together simply because the model cannot represent the non-English text well. If your data is “mostly English,” you still need to detect and handle non-English segments during ingestion to avoid polluting the vector index. A second limitation is that being “small” is a tradeoff. Smaller models are generally fast and cost-effective, but they can struggle with fine-grained distinctions when many documents are very similar. For example, two API reference pages might share 90% overlapping phrasing but differ in a single parameter meaning. The embeddings may be close enough that both pages appear in the same top-k results, which is not wrong, but it may force you to add reranking logic or stronger metadata filters. In systems using [Milvus](https://milvus.io/) or [Zilliz Cloud](https://zilliz.com/cloud), this often means leaning on structured fields (product, version, language, permissions) to narrow the candidate set before vector similarity is applied. A third limitation is document structure handling. The model treats whatever you feed it as plain text; it does not inherently “understand” Markdown tables, code blocks, or HTML layout in a special way. If you embed raw scraped pages with lots of navigation text, repeated headers, or code snippets, retrieval quality can suffer. The fix is usually simple but non-optional: clean the text, remove boilerplate, chunk at logical boundaries, and store helpful metadata. Most retrieval issues blamed on “the model” are actually pipeline issues: chunking strategy, preprocessing consistency, and evaluation methodology. For more information, click here：