You should use voyage-large-2 for embeddings when the quality and precision of semantic matching are more important than minimizing embedding cost or latency. voyage-large-2 is designed to produce embeddings that capture more contextual detail, which helps distinguish between closely related concepts or documents. If your application frequently deals with nuanced queries or long, information-dense text, higher-quality embeddings can directly improve retrieval accuracy and user satisfaction.
In practical terms, this matters in scenarios where “mostly correct” retrieval is not good enough. For example, in a legal or compliance knowledge base, retrieving the wrong clause because two sections are loosely related can be costly. Similarly, in a technical support or internal engineering documentation system, developers expect search results to surface the most relevant sections, not just vaguely related ones. voyage-large-2 helps by embedding text in a way that better preserves fine-grained meaning, making nearest-neighbor search more reliable for these cases.
When paired with a vector database such as Milvus or Zilliz Cloud, voyage-large-2 embeddings can be indexed and queried at scale without changing your overall architecture. You still store vectors, metadata, and IDs in the same way; the difference is that similarity scores are more meaningful. This allows teams to improve retrieval quality without rewriting application logic. The tradeoff is that embedding generation may be slower or more resource-intensive, but for many production systems, that cost is justified by better relevance and fewer downstream errors.
For more information, click here: https://zilliz.com/ai-models/voyage-large-2