Yes,voyage-code-2 works well with Milvus because the integration is designed around the standard embedding workflow: generate vectors with the model, store them in Milvus, and run similarity search with metadata filters. Milvus provides a built-in VoyageEmbeddingFunction (in PyMilvus) that explicitly supports selecting Voyage model names such as voyage-code-2, which makes the “model call + vector pipeline” feel like normal application code rather than a custom ML stack. In other words, voyage-code-2 supplies the embeddings and Milvus supplies the indexing and retrieval engine, which is exactly what you want for semantic code search at scale.
The practical reason this pairing works is that code retrieval needs more than just vector math; it needs operational features like schema design, filtering, and fast top-k search over large collections. With Milvus, you can store each embedded code unit (often one function/class/snippet) along with metadata fields like repo, path, language, symbol, commit, and owner_team. Then you can filter before (or alongside) similarity search—for example: “only search repo=payments and language=go” or “exclude generated code.” That tends to improve relevance and reduces noise. If you don’t want to run and scale Milvus yourself, Zilliz Cloud (managed Milvus) gives you the same core capabilities with managed operations.
A good mental model is: voyage-code-2 improves representation (semantic embeddings for code + code-adjacent text), while Milvus improves retrieval mechanics (indexing, filtering, and query performance). To get the best results, you still need to choose sensible chunking boundaries (function-level is a common starting point), store enough metadata to filter effectively, and keep a small evaluation set of real developer queries to check top-k quality after each ingestion change. But if you’re asking whether the “plumbing” is solid: Milvus explicitly supports Voyage embedding functions and model selection, and Zilliz’s voyage-code-2 guide positions the model as “Milvus Integrated,” so the end-to-end pairing is a first-class path.
For more information, click here: https://zilliz.com/ai-models/voyage-code-2