jina-embeddings-v2-base-en outputs embeddings with a fixed dimension of 768. This means that every piece of text processed by the model, regardless of length, is represented as a vector containing 768 numerical values. This fixed dimensionality is essential for similarity search because vector databases require all vectors in a collection to have the same shape.
From an implementation perspective, knowing the embedding dimension is critical when setting up storage and indexing. When creating a collection or index in a vector database such as Milvus or Zilliz Cloud, developers must explicitly define the vector field with a dimension of 768. If the dimension does not match the model output, insert and query operations will fail. For this reason, teams usually verify the dimension during early integration and treat it as part of their schema contract.
The 768-dimensional size offers a practical balance between semantic expressiveness and efficiency. It is large enough to capture nuanced meaning in English text, including longer passages, while remaining manageable in terms of storage and search performance. When combined with efficient indexing and filtering in Milvus or Zilliz Cloud, these embeddings support scalable semantic search and RAG workloads without excessive infrastructure overhead.
For more information, click here: https://zilliz.com/ai-models/jina-embeddings-v2-base-en