Common use cases for voyage-2 center around tasks where understanding the meaning of text matters more than exact wording. One of the most frequent examples is semantic search over documentation, knowledge bases, or internal wikis. By embedding documents and queries with voyage-2, developers can build search systems that return relevant sections even when users phrase questions differently from the source text. This is especially useful in developer docs, customer support portals, and internal tooling.
Another common use case is retrieval-augmented generation (RAG). In these systems, voyage-2 is used to retrieve relevant context from a large corpus before passing that context to a language model for answer generation. The quality of the retrieved context strongly affects the quality of the final answer, which is why stable and semantically meaningful embeddings are important. voyage-2 is well-suited for this role because it produces consistent embeddings that can be efficiently searched at scale.
voyage-2 is also used for clustering, deduplication, and similarity analysis. For example, teams might embed support tickets to identify recurring issues or group similar feedback together. In all of these scenarios, embeddings are usually stored and queried using a vector database such as Milvus or Zilliz Cloud. The database enables fast similarity operations, while voyage-2 ensures that “similar” in vector space corresponds to “similar” in meaning. This combination supports a wide range of text-driven applications without requiring complex custom logic.
For more information, click here: https://zilliz.com/ai-models/voyage-2