voyage-code-2 integrates with vector databases through a clean separation of responsibilities: the model generates embeddings, and the database stores, indexes, and searches them. After generating embeddings for code and documentation, developers insert those vectors into a vector database along with metadata such as file path, repository name, programming language, and ownership. This makes the embeddings searchable and manageable at scale.
In a typical setup, a collection is created with a vector field sized to the embedding dimension produced by voyage-code-2. Each record includes the vector plus metadata and the original text. At query time, the user’s input is embedded using the same model, and the vector database performs a similarity search to return the nearest matches. This workflow is well-supported by vector databases such as Milvus and its managed counterpart, Zilliz Cloud.
The advantage of this integration is flexibility. Developers can evolve their retrieval logic—changing chunking strategies, adding metadata filters, or adjusting search parameters—without changing the embedding model. voyage-code-2 provides the semantic representation, while Milvus or Zilliz Cloud provides the infrastructure needed for fast, reliable search. Together, they form a practical foundation for semantic code search and code-aware RAG systems.
For more information, click here: https://zilliz.com/ai-models/voyage-code-2