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Each Vector database holds a single collection of records that share a common schema and vector properties.

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Collection

A collection is defined by its vector properties and its attribute schema. The vector properties — dimensions and distance metric — are set at creation time and are immutable.
PropertyDescription
DimensionsThe number of dimensions for all vectors in the collection. All records must have a vector with exactly this many dimensions.
Distance metricThe metric used to compute similarity between vectors. Either l2 (Euclidean distance) or dot_product.

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Records

Each record in a collection has:
  • A string ID — a unique, user-provided identifier up to 64 bytes.
  • A set of attributes — typed key-value pairs defined by the collection schema.
  • A vector — a special attribute named vector that holds a dense vector of f32 values with the collection’s configured number of dimensions.

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Attributes

Attributes are typed fields on a record. Each attribute in the schema has a name, a type, and a flag indicating whether it is indexed.
TypeDescription
StringUTF-8 string
Int6464-bit signed integer
Float6464-bit floating point
BoolBoolean
TextUTF-8 text, tokenized and full-text indexed for BM25 search

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Indexed vs. non-indexed attributes

An attribute can be marked as indexed in the collection schema. Indexed attributes are maintained in an inverted index that maps attribute key-value pairs to the set of matching records. This enables efficient attribute-based filtering during queries — for example, filtering results where category="shoes". Non-indexed attributes are stored with the record but cannot be used in filter predicates efficiently. Use non-indexed attributes for data that you want to retrieve but don’t need to filter on.

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Text fields

Attributes of type Text are always full-text indexed; the indexed flag does not apply to them. On write, Vector tokenizes the text and maintains a BM25 index over its terms. A query can then score records by BM25 relevance against a text field instead of by vector similarity, which makes Vector usable for keyword search as well as semantic search. The two scoring modes are separate per query.

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Vector

The vector attribute is a reserved field that holds the record’s dense embedding. It is a vector of f32 values whose length must exactly match the collection’s configured dimensions. This is the field used for similarity search — queries find the nearest records by comparing their vectors using the collection’s distance metric.