To index text-embedding-3-small vectors in Milvus, you define a collection with a fixed-dimension vector field, insert embeddings with IDs/metadata, and create an ANN index optimized for your metric and latency goals. The critical constraint is that every vector in a Milvus collection must share the same dimension, so you should decide the embedding dimension you will use (including any optional shortening) before creating the collection. Once the collection exists, you can ingest data in batches, build an index, and then query with a similarity metric such as cosine similarity or inner product.
A typical schema looks like: id (primary key), embedding (FLOAT_VECTOR), plus scalar fields like doc_id, source, language, updated_at, or tenant_id. You generate embeddings with text-embedding-3-small for each text chunk and insert rows like (id, embedding, doc_id, ...). After enough data is inserted, you create an index on the vector field. The specific index type and parameters depend on scale and latency needs, but the basic workflow is consistent: (1) insert, (2) flush, (3) create index, (4) load collection into memory for querying. If you expect continuous ingestion, you’ll also plan for incremental updates and periodic compaction, and you’ll store enough metadata to support filtering and debugging.
Milvus is designed for this exact workflow, and if you want managed operations, Zilliz Cloud offers managed Milvus with the same conceptual steps. The practical tips that usually matter most are: batch inserts (e.g., hundreds to thousands of vectors per call), keep your metadata clean (types consistent, avoid unbounded text fields in scalar columns), and choose a metric that matches your embedding usage (cosine is common; inner product is also common if you normalize vectors consistently). Also, test indexing and search with realistic K and filter patterns—filters can change performance characteristics. Once indexed, you’ll query by embedding the user query with text-embedding-3-small, then calling Milvus search with the query vector, topK, metric type, and (optionally) a boolean expression filter like language == "en" AND source == "docs".
For more information, click here： https://zilliz.com/ai-models/text-embedding-3-small