Yes, all-mpnet-base-v2 is a textbook fit for vector databases because it outputs fixed-length dense vectors that are designed to be searched by similarity. In most implementations, all-mpnet-base-v2 produces 768-dimensional embeddings, which vector databases can store and index efficiently using approximate nearest neighbor (ANN) algorithms. The standard pattern is: embed each document chunk → store the vector with metadata → embed the user query → run a nearest-neighbor search to retrieve top-k candidates. This is exactly the workflow vector databases are built for, and it scales cleanly from thousands to millions of embeddings.
The key production considerations are schema design and consistency. Decide which similarity metric you’ll use (cosine similarity is common), normalize vectors consistently if needed, and store metadata that lets you filter. For example, store lang, product, version, doc_type, and access_scope, then filter before vector search to keep results relevant and compliant. This is especially important because semantic embeddings can retrieve conceptually similar but incorrect results when you have multiple versions of docs or similar feature names. Filters reduce that risk without needing a different model.
A vector database such as Milvus or Zilliz Cloud makes these patterns operationally simple. You can create separate collections for different embedding versions, build ANN indexes tuned to your latency/recall needs, and run online A/B tests by switching which collection a query hits. You also get scalable ingestion and the ability to attach scalar indexes for fast metadata filtering. The model produces the semantic space; the database makes that space searchable and manageable in production.
For more information, click here: https://zilliz.com/ai-models/all-mpnet-base-v2