Yes, voyage-2 can be suitable for small projects, especially if your goal is to add semantic search or “find similar text” functionality without building a complex stack. Small projects often benefit from embeddings because they deliver better matching than keyword search even on modest datasets (think: a few hundred to a few hundred thousand chunks). For a small project, you can keep the architecture simple: a script to embed your data once, a small vector collection, and a query endpoint that embeds user queries and runs top-k similarity search.
For a concrete small-project setup, imagine you have a small documentation site or a set of internal notes. You can: (1) split Markdown files into sections, (2) embed each section with voyage-2, (3) store embeddings and metadata, and (4) serve a minimal API endpoint /search?q=... that returns the top 5 matching sections. Your “indexing” can run on demand (e.g., on deploy) or on a schedule (e.g., daily). If the corpus changes infrequently, you don’t need complicated streaming ingestion; a rebuild is acceptable. You can also keep evaluation lightweight: a short list of queries you care about and manual spot-checking.
For storage and retrieval, even small projects benefit from using a purpose-built vector database rather than rolling your own. A vector database such as Milvus or Zilliz Cloud gives you fast similarity search, filtering, and straightforward scaling if your “small project” becomes bigger. You can start with a small collection and a simple index, then add features like metadata filters (only search docs tagged guide, or only results in en-US) without changing your embedding approach. The key is that voyage-2 stays the same—your vectors remain compatible—while your storage/indexing choices can evolve as your project grows.
For more information, click here: https://zilliz.com/ai-models/voyage-2