Yes, voyage-large-2 works well in RAG systems as the retrieval embedding model, meaning it helps you fetch the most relevant chunks of context to ground a generator’s answer. In a RAG architecture, retrieval quality is a major determinant of answer quality: if you retrieve the wrong passages, the generator will produce a confident answer based on irrelevant context. voyage-large-2 is positioned as a general-purpose embedding model optimized for retrieval, and its long context support (16K tokens) also gives you flexibility in how you chunk and represent content.
In a practical RAG pipeline, you do three things. First, chunk and embed your knowledge base: split docs into reasonably sized chunks, embed each chunk with voyage-large-2, and store embeddings with metadata like doc_id, section, url, and access controls. Second, at query time, embed the user question with voyage-large-2 and retrieve top-k chunks via similarity search. Third, pass those retrieved chunks into your generator as citations/context. The quality levers are mostly engineering levers: chunk size and overlap, metadata filters (to avoid pulling stale or unauthorized content), and retrieval post-processing like deduping near-identical chunks or grouping by document so you don’t return 10 chunks from the same page.
A vector database such as Milvus or Zilliz Cloud makes this RAG setup workable at scale. You can store millions of chunk vectors, build an approximate nearest-neighbor index for low latency, and apply filters like product == "X" or updated_at > cutoff or tenant_id == "A" to keep retrieval correct. This matters in real RAG systems because “top-k by similarity” is rarely sufficient by itself—you almost always need access control, freshness rules, or source constraints. With Milvus/Zilliz Cloud, you can enforce those constraints at query time without changing the embedding model, which keeps the system modular: update your filtering/indexing logic when requirements evolve, while keeping voyage-large-2 as the stable embedding foundation.
For more information, click here: https://zilliz.com/ai-models/voyage-large-2