voyage-code-2 generates semantic embeddings for code and code-related text so you can retrieve relevant code snippets (or documentation) by meaning. Concretely, you give it an input—like a function body, a docstring, an issue description, or a natural-language query—and it outputs a vector. Once you have vectors for your corpus, you can compare them to a query vector and return the nearest neighbors. That is what powers semantic code search (“find me the function that validates an auth token”), similarity lookup (“find code like this snippet”), and retrieval components for RAG systems that answer developer questions using the codebase as grounding context. :contentReference[oaicite:3]{index=3}
A practical way to understand “what it does” is to follow the retrieval loop. During ingestion, you decide what units you want to retrieve (for example, each function as one record), then you embed each unit with voyage-code-2 and store {id, embedding, text, metadata}. Metadata might include file path, repository, language, module name, commit hash, and ownership. At query time, you embed the user’s query and run similarity search to get top-k candidates. Many embedding ecosystems also support a “query vs document” mode (often called input_type), which can improve retrieval ranking if you embed queries as queries and corpus items as documents. The result is that voyage-code-2 becomes the semantic “addressing system” for your codebase. :contentReference[oaicite:4]{index=4}
When you connect that loop to a vector database such as Milvus or Zilliz Cloud, you get the part that developers care about: fast retrieval at scale with filtering. For example, you can filter by repo="payments-service" and lang="python" before similarity scoring, or filter out generated code. This is often the difference between a demo that “sort of works” and a tool developers trust daily. voyage-code-2’s role is the embedding step; Milvus/Zilliz Cloud’s role is indexing and searching those embeddings efficiently while your application enforces rules and presents results. :contentReference[oaicite:5]{index=5}
For more information, click here: https://zilliz.com/ai-models/voyage-code-2