Yes, all-mpnet-base-v2 is a sentence embedding model: it is designed to take a sentence or short passage as input and output a dense vector that represents its meaning. It is commonly used through sentence embedding libraries that wrap tokenization, batching, and pooling. The resulting embeddings are intended for similarity-based tasks rather than text generation. In other words, you use it to answer “which texts are most similar to this query?” rather than “what text should be generated next?”
Technically, it is an encoder-only Transformer model adapted for sentence embeddings through training objectives that align semantically similar texts. The “base” in the name indicates a standard-size encoder architecture, which generally has more capacity than lightweight MiniLM variants. In practice, the model outputs one vector per input text, and developers often normalize vectors and use cosine similarity for comparisons. This makes it easy to plug into semantic search pipelines, clustering workflows, and retrieval layers for RAG.
In production, sentence embedding models are most useful when combined with a storage and retrieval system that can search vectors efficiently. A vector database such as Milvus or Zilliz Cloud provides indexing, filtering, and scalable search. You embed your corpus once (offline), store vectors plus metadata, then embed queries (online) and retrieve top-k similar chunks. This architecture scales cleanly and is easy to reason about: the model defines the vector space, and the database makes that space searchable. It’s also easy to evaluate: you can measure retrieval metrics, swap models, and keep the same database schema and query logic.
For more information, click here: https://zilliz.com/ai-models/all-mpnet-base-v2