voyage-large-2 generates text embeddings by processing input text through a larger and more expressive neural network that is optimized for semantic representation. From a developer’s point of view, the workflow is simple: you send text to the embedding API, and the model returns a fixed-length vector of floating-point values. Internally, however, the model is designed to capture deeper contextual relationships across the input, which results in embeddings that encode more semantic information per vector.
The model analyzes the input text as a sequence of tokens and builds contextual representations that account for how words relate to each other across the entire passage. These representations are then aggregated into a single dense vector. Because voyage-large-2 has greater capacity than smaller embedding models, it can preserve subtle distinctions—such as differences in intent, scope, or emphasis—that might otherwise be flattened. For developers, the important property is consistency: the same text always produces the same vector for a given model version, which makes embeddings comparable across time and datasets.
Once generated, these embeddings are typically stored rather than recomputed. A common pattern is to generate embeddings in batch for documents and store them in a vector database such as Milvus or Zilliz Cloud. At query time, only the query text needs to be embedded, and the resulting vector is compared against the stored vectors. voyage-large-2 focuses on producing high-quality vectors, while the vector database handles indexing and similarity search. This separation allows developers to take advantage of a more powerful embedding model without complicating their retrieval infrastructure.
For more information, click here: https://zilliz.com/ai-models/voyage-large-2