voyage-large-2 integrates with Milvus by producing fixed-length embedding vectors that Milvus can store, index, and search using nearest-neighbor similarity. The integration is straightforward: you embed documents (or chunks) using voyage-large-2, insert the resulting vectors into a Milvus collection, and at query time you embed the user’s query and run a top-k vector search. The key schema detail is that voyage-large-2 produces 1536-dimensional embeddings, so your Milvus collection’s vector field must be defined with dim=1536. If you get that dimension wrong, inserts will fail or retrieval will be meaningless.
A typical implementation has two paths: ingestion and query. In ingestion, you chunk text (e.g., 500–1,000 tokens with 10–20% overlap), call the embedding API (often batched), and insert records like {id, embedding, doc_id, chunk_id, text, title, url, lang, updated_at}. In the query path, you embed a short query string, then call search with parameters like top_k=10, a similarity metric (often cosine/dot-product style), and an optional filter expression (e.g., lang == "en" && access_level <= user_level). Milvus returns IDs and scores; your app fetches the corresponding chunk text and metadata to display results or feed a downstream pipeline. A common best practice is to make (doc_id, chunk_id) stable so you can upsert embeddings when docs change and delete embeddings when content is removed.
If you want a smoother developer experience, you can run this pipeline with either Milvus directly or Zilliz Cloud (managed Milvus). The architectural idea stays the same: voyage-large-2 handles “text → vector,” and Milvus/Zilliz Cloud handles “vector → index → search.” The database side is where you tune for your workload: choose an ANN index type and parameters to balance recall and latency, partition by tenant or source to keep filtered searches fast, and monitor memory because 1536-d vectors increase index footprint compared to smaller embeddings. Integration is “easy” in terms of code, but “production-ready” depends on these schema and indexing choices.
For more information, click here: https://zilliz.com/ai-models/voyage-large-2