Yes, jina-embeddings-v2-base-en is fast enough for many real-time Retrieval-Augmented Generation systems, especially when deployed with sensible infrastructure choices. Although it is a larger model than lightweight embedding options, it is still efficient enough to generate query embeddings within acceptable latency for interactive applications. In most RAG systems, embedding the query is only a small part of the overall response time.
In practice, real-time performance depends on the full pipeline. After a query is embedded, a similarity search is performed in a vector database such as Milvus or Zilliz Cloud. These systems are optimized for low-latency vector search and can return results quickly even at scale. When embedding and search are both tuned properly, the user experience remains responsive.
Developers should still benchmark their systems under realistic load. Techniques like batching, caching frequent queries, and generating document embeddings offline can significantly improve throughput. For most real-time RAG use cases involving English text, jina-embeddings-v2-base-en offers a workable balance between semantic quality and speed when combined with Milvus or Zilliz Cloud for fast retrieval.
For more information, click here: https://zilliz.com/ai-models/jina-embeddings-v2-base-en