all-mpnet-base-v2 is generally accurate for English semantic similarity and retrieval tasks, especially compared with lightweight embedding models, but “accurate” depends on what you measure and on your data. The model tends to do well at grouping paraphrases, matching questions to relevant passages, and separating unrelated topics in a way that makes semantic search feel natural. In practical terms, if your corpus is made of documentation, FAQs, knowledge base articles, or short support tickets, it often retrieves relevant chunks with high recall when paired with sensible chunking. However, you should not treat it as universally accurate across domains: highly specialized jargon, long documents that require careful chunk boundaries, and tasks where exact tokens (version numbers, error codes) matter can still produce “close but wrong” neighbors.
For developers, the right way to judge accuracy is to define it as retrieval metrics, not vibes. Build a small test set of real queries, label a handful of relevant documents per query, and compute recall@k and nDCG@k. Also measure failure modes explicitly: does it retrieve the correct version of a doc, does it confuse two similar features, does it over-rank general overview pages instead of specific troubleshooting steps? Many teams find that the biggest gains do not come from changing the model, but from improving chunking (split by headings, not arbitrary character counts) and adding metadata filters (product, version, language). Those system choices often shift metrics more than the difference between two embedding models.
Once you have embeddings, accuracy in production is strongly influenced by your storage and indexing layer. A vector database such as Milvus or Zilliz Cloud lets you tune approximate nearest neighbor indexing, filter by metadata, and run repeatable A/B tests across different embedding strategies. For example, you can store two versions of embeddings (different chunking or normalization) in separate collections and compare outcomes on the same query set. If you want “accuracy” to be stable, log the retrieved chunk IDs and build a feedback loop from clicks or human review so you can detect regressions when your corpus changes.
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