To get started with voyage-large-2, you typically (1) generate embeddings for your documents, (2) store them in a vector database, and (3) embed queries and run similarity search. The Zilliz model guide shows a concrete path: using PyMilvus’s built-in embedding function wrapper for voyage-large-2, generating document and query embeddings, and then inserting and searching them in Zilliz Cloud (managed Milvus). This is a good beginner-to-production workflow because it mirrors how real semantic search systems are built: precompute document vectors, keep query vectors online, and let the database do fast nearest-neighbor search.
A practical first project is “search my docs.” Take a small corpus (maybe 20–50 pages), split it into chunks (for example, by headings with a max size target), and embed each chunk. Store each chunk with metadata like doc_id, title, url, chunk_index, text, and updated_at. Then build a query endpoint that: embeds the user’s query, runs top-k vector search, and returns the chunk text plus metadata so you can display results. When you test, don’t rely on a single happy-path query—create 20–30 queries that reflect how users actually ask questions, including abbreviations, typos, and alternate phrasing. If results feel “too broad,” reduce chunk size. If results miss context, increase chunk size or add overlap. This iteration is normal and usually more impactful than changing models.
For storage, start with a vector database such as Milvus or Zilliz Cloud. The guide’s code illustrates the core operations you’ll implement: create a collection with the model’s embedding dimension, insert vectors alongside text, and run search to retrieve matches. In production, you’ll extend this with: batching (embed many chunks per request), retry/backoff on embedding calls, idempotent upserts keyed by (doc_id, chunk_id), and a reindex strategy for when you change chunking or model versions. But you don’t need all that on day one—get a small end-to-end prototype working, then harden the pipeline as the dataset and traffic grow.
For more information, click here: https://zilliz.com/ai-models/voyage-large-2