jina-embeddings-v2-small-en has several practical limitations that developers should understand before using it in production. The most important limitation is that it is English-only. If your data contains mixed languages or non-English content, the embedding quality will drop quickly because the model was not trained to represent multilingual semantics. Another limitation is that it is a general-purpose embedding model, which means it may not fully capture highly specialized domain language such as medical terminology, legal clauses, or internal company jargon without careful preprocessing and evaluation.
From a system design perspective, developers should also be aware that smaller embedding models trade some semantic precision for speed and efficiency. jina-embeddings-v2-small-en performs well for common semantic search tasks, but it may struggle to distinguish very subtle differences between near-duplicate texts or deeply technical passages. This often shows up when many documents are similar and only differ by small details. In these cases, retrieval results stored in a vector database such as Milvus or Zilliz Cloud may look reasonable but still require reranking or filtering logic at the application layer.
Another limitation is that the model does not understand document structure or formatting. Headings, tables, and code blocks are treated as plain text unless you explicitly handle them during preprocessing. Long documents must be chunked, and poor chunking can reduce retrieval quality more than the model itself. Developers should plan for text cleaning, chunk sizing, and metadata usage when storing vectors in Milvus or Zilliz Cloud. Understanding these constraints early helps avoid misattributing system issues to the model rather than to pipeline design.
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