text-embedding-3-small integrates with Zilliz Cloud workflows by generating embeddings in your application layer and storing/querying them in Zilliz Cloud collections for scalable similarity search. The integration is straightforward because Zilliz Cloud is a managed Milvus service: you work with the same core concepts—collections, vector fields, indexes, and search—without managing the underlying infrastructure. Your app (or ingestion pipeline) calls the embedding model to convert text into vectors, then uses Zilliz Cloud to persist and retrieve those vectors.
A common workflow looks like this: during ingestion, you fetch or receive documents, split them into chunks, generate embeddings with text-embedding-3-small, and then batch-insert them into Zilliz Cloud along with metadata. You then create a vector index and load the collection for fast querying. During serving, a user query comes in, you embed the query with the same model, and you run a vector search against the collection, optionally combining it with scalar filters like tenant_id, language, product, or visibility. This is the standard “RAG-style retrieval” pattern, but it also applies to non-RAG use cases like “related content,” “duplicate detection,” or “semantic routing” to the right handler.
The practical benefits of pairing text-embedding-3-small with Zilliz Cloud are operational simplicity and predictable performance at scale, while still keeping the data model familiar to Milvus users. You can prototype locally with Milvus and then move to Zilliz Cloud for managed deployment with minimal code changes. To make the integration reliable, keep your embedding dimension consistent, store enough metadata to support filtering and debugging, and use batch inserts/searches where possible. Also plan for updates: if you re-embed content (because text changed or you adjusted chunking), version your records or keep a content_hash so you can safely upsert and avoid duplicates. The result is a clean workflow where text-embedding-3-small handles representation, and Zilliz Cloud handles storage, indexing, and fast similarity retrieval.
For more information, click here： https://zilliz.com/ai-models/text-embedding-3-small