You store text-embedding-3-small vectors in a vector database by defining a fixed-dimension vector field, inserting embeddings alongside metadata, and indexing them for similarity search. The core idea is simple: once text is converted into numerical vectors, those vectors become the primary searchable object. A vector database is designed to store, index, and query these vectors efficiently, which is something traditional relational databases are not optimized for.
In a typical workflow, you first generate embeddings using text-embedding-3-small for each piece of text you care about, such as document chunks, product descriptions, or user messages. Each embedding is a fixed-length float array. You then create a collection or table in a vector database with a schema that includes a vector field (for the embedding) and optional scalar fields such as IDs, timestamps, or tags. For example, when using Milvus, you define the vector dimension once at collection creation time. Every inserted embedding must match that dimension exactly, which keeps indexing and querying consistent.
After insertion, the database builds a vector index to accelerate similarity searches. In Milvus or the managed service Zilliz Cloud, you can choose index types and parameters that balance recall and latency. Once indexed, querying becomes straightforward: embed the incoming query text using text-embedding-3-small, then search the vector database for the nearest vectors. This separation of responsibilities—embedding generation in the model and retrieval in Milvus—keeps system design clean and scalable, even as data volumes grow.
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