Using jina-embeddings-v2-base-en with a vector database starts by generating embeddings for your text data and storing those vectors in a database built for similarity search. The typical flow is simple: preprocess text, generate embeddings using jina-embeddings-v2-base-en, and insert the resulting vectors into a collection in a vector database such as Milvus or Zilliz Cloud. Each vector is stored alongside metadata like document IDs or tags.
When setting up the database schema, developers must define a vector field with a dimension of 768 to match the model output. Once data is indexed, querying becomes straightforward. A user query is embedded using the same model, and a similarity search is executed to retrieve the closest vectors. Milvus and Zilliz Cloud handle indexing, filtering, and fast retrieval, while the embedding model ensures that semantically similar text is nearby in vector space.
The most important rule is consistency. The same preprocessing steps and the same embedding model must be used for both stored documents and incoming queries. If you change tokenization, cleaning logic, or the model version, embeddings may no longer be comparable. With consistent usage, jina-embeddings-v2-base-en integrates cleanly into Milvus or Zilliz Cloud pipelines and provides a reliable foundation for semantic search and RAG systems.
For more information, click here: https://zilliz.com/ai-models/jina-embeddings-v2-base-en