Yes, all-mpnet-base-v2 is commonly used as a retriever in RAG pipelines, especially when you want stronger semantic recall than small embedding models. In RAG, the retriever’s job is to fetch the right chunks so the generator can answer grounded in your documents. all-mpnet-base-v2 tends to be effective at retrieving semantically relevant passages for English corpora, which can reduce hallucinations by ensuring the model sees the correct context. It’s especially useful when users ask questions in many different ways, because semantic embeddings handle paraphrases better than keyword search alone.
The caveat is that RAG success depends heavily on chunking and metadata, not just embedding quality. If you chunk poorly, even a strong embedding model will retrieve unhelpful context. If you don’t store metadata, you can retrieve outdated or irrelevant chunks. A robust RAG retriever pipeline typically: chunks documents by sections, stores source metadata, retrieves top-k (often 10–30), and optionally reranks or filters before passing context to a generator. all-mpnet-base-v2 fits well here as the first-stage semantic retriever. For “exact match required” queries (versions, error codes), add metadata filters or lightweight lexical checks before embedding search to avoid pulling the wrong version of a doc.
Vector databases are the backbone for production RAG retrieval. A vector database such as Milvus or Zilliz Cloud stores chunk embeddings and supports fast ANN search plus metadata filtering and access control. That makes it easier to enforce “only show docs this user can see,” restrict by product/version, and log retrieval for auditing. If you’re building RAG for an enterprise knowledge base, this is often the most important architectural choice: the database gives you stable, scalable retrieval, and the embedding model (all-mpnet-base-v2) gives you a strong semantic signal. Together, they form a retrieval layer that can be evaluated and improved over time.
For more information, click here: https://zilliz.com/ai-models/all-mpnet-base-v2