You store text-embedding-3-large vectors in a vector database by defining a fixed-dimension vector field, inserting embeddings together with metadata, and building an index for similarity search. The process is conceptually simple: the embedding model converts text into numerical vectors, and the vector database is responsible for storing, indexing, and retrieving those vectors efficiently.
In practice, the workflow usually starts with text preprocessing. Long documents are split into chunks (for example, paragraphs or sections), and each chunk is passed to text-embedding-3-large to generate an embedding. You then define a collection schema in a vector database such as Milvus with a FLOAT_VECTOR field whose dimension matches the embedding output, plus scalar fields like id, doc_id, language, source, or timestamps. Each inserted row contains one embedding and its associated metadata. Batch insertion is strongly recommended because it reduces ingestion overhead and speeds up indexing.
Once data is inserted, you create a vector index and load the collection for querying. In Milvus, indexing and search parameters can be tuned later without changing how embeddings are generated. If you prefer not to manage infrastructure, Zilliz Cloud offers the same Milvus data model as a managed service. In both cases, the key requirement is consistency: all embeddings stored in a collection must have the same dimension and must be generated using the same model configuration. This ensures that similarity searches behave predictably as your dataset grows.
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