Yes! beginners can use voyage-2, as long as they’re comfortable making an API call and handling a JSON response. voyage-2 is an embedding model: you send it text, and it returns a numeric vector (an array of floats). You do not need to understand how the model is trained to get value from it; you mainly need to know what embeddings are for (semantic similarity) and where you’ll store and search them (often a vector database). A beginner-friendly way to think about it is: “voyage-2 turns text into numbers so my app can measure meaning.”
Where beginners usually get stuck isn’t the embedding call itself—it’s the rest of the pipeline. A typical beginner workflow looks like this: (1) split your documents into chunks (for example, 300–800 tokens each), (2) call the Voyage embeddings API with a list of chunk strings, (3) store the returned vectors along with metadata (document ID, URL, title, chunk text), and (4) at query time, embed the query and run a nearest-neighbor search. voyage-2’s vector dimensionality is consistent, which makes schema decisions straightforward: you create a single vector field with that dimension, and you keep the original text in a separate field for display. If you’re learning, start with a tiny dataset (like 50–200 chunks) so you can validate that “similar questions retrieve the right paragraphs” before you scale.
Beginners also benefit from pairing voyage-2 with a vector database that handles indexing and similarity search for them. A vector database such as Milvus or Zilliz Cloud (managed Milvus) lets you insert embeddings, build an index (e.g., HNSW/IVF depending on your needs), and query top-k nearest vectors with optional metadata filtering. That means you can focus on: choosing chunk sizes, deciding what metadata to store, and writing the glue code—rather than implementing approximate nearest neighbor search from scratch. If you can build a basic REST call and write a script that iterates over files, you’re already most of the way there.
For more information, click here: https://zilliz.com/ai-models/voyage-2