Yes, all-mpnet-base-v2 is a strong choice for semantic search, especially for English text, because it produces embeddings that capture meaning well enough to retrieve relevant results even when keywords don’t match. In a typical semantic search setup, you embed documents (or document chunks) into vectors, embed the user’s query into a vector, and retrieve the nearest neighbors by cosine similarity or inner product. all-mpnet-base-v2 tends to perform well as a first-stage retriever because it balances representational quality with deployment simplicity: you can run it locally, it has predictable output shape, and it integrates cleanly into standard retrieval pipelines.
The main caveat is that semantic search is a system, not just an embedding model. If you embed whole documents, you will often get mediocre results because one vector has to represent multiple topics. If you chunk documents by headings and keep chunks at a moderate size, the retriever becomes far more precise. Another common issue is “exact match constraints”: semantic embeddings can retrieve conceptually related text that is wrong in detail. For example, a query about “API v2.6 rate limits” might retrieve a general “rate limits” doc for a different version. That’s not a reason to avoid all-mpnet-base-v2; it’s a reason to add metadata like version and filter or rerank accordingly. If your corpus is noisy (logs, tables), preprocessing also matters: strip boilerplate, normalize whitespace, and keep meaningful tokens (error codes, parameter names) intact.
A vector database makes this practical at scale. With a vector database such as Milvus or Zilliz Cloud, you can store vectors alongside metadata and query with filters and tuned ANN indexes to keep latency low. This setup also supports real evaluation: you can replay a test query set, compare retrieval quality after corpus updates, and adjust index/search parameters without re-architecting the whole system. For semantic search, the combination of a strong embedding model and a well-tuned vector database is the standard production pattern.
For more information, click here: https://zilliz.com/ai-models/all-mpnet-base-v2