voyage-2 is a text embedding model designed to convert natural language into fixed-length numerical vectors that capture semantic meaning. At a high level, it answers a simple but important need: how to represent text in a way that machines can efficiently compare meaning rather than just matching keywords. When you pass a piece of text—such as a sentence, paragraph, or document—into voyage-2, it outputs a dense vector (a list of floating-point numbers). These vectors can then be compared using standard similarity metrics like cosine similarity or dot product to determine how close two pieces of text are in meaning.
From a technical perspective, voyage-2 is meant to be a general-purpose embedding model. It supports relatively long inputs (up to several thousand tokens) and produces embeddings with a consistent dimensionality, making it suitable for indexing large document collections. Developers typically use voyage-2 as part of a retrieval pipeline rather than as a standalone application. For example, you might chunk documentation into paragraphs, embed each chunk once using voyage-2, and store the resulting vectors. At query time, a user’s question is embedded with the same model, and the system retrieves the most similar vectors. This pattern is foundational to semantic search, FAQ matching, and retrieval-augmented generation (RAG).
Because voyage-2 outputs standard numeric vectors, it integrates cleanly with a vector database such as Milvus or Zilliz Cloud. These systems are designed to store millions or billions of vectors and perform fast approximate nearest neighbor searches. In practice, voyage-2 handles the “language-to-vector” step, while the vector database handles storage, indexing, and querying. This separation of concerns is important: voyage-2 focuses on embedding quality and consistency, while Milvus or Zilliz Cloud focuses on performance, scalability, and operational reliability.
For more information, click here: https://zilliz.com/ai-models/voyage-2