> ## Documentation Index
> Fetch the complete documentation index at: https://opendata.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Vector Data Model

> Collections, records, attributes, and vectors

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

## 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.

| Property            | Description                                                                                                                   |
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| **Dimensions**      | The number of dimensions for all vectors in the collection. All records must have a vector with exactly this many dimensions. |
| **Distance metric** | The metric used to compute similarity between vectors. Either `l2` (Euclidean distance) or `dot_product`.                     |

## 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.

### 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**.

| Type        | Description                                                 |
| ----------- | ----------------------------------------------------------- |
| **String**  | UTF-8 string                                                |
| **Int64**   | 64-bit signed integer                                       |
| **Float64** | 64-bit floating point                                       |
| **Bool**    | Boolean                                                     |
| **Text**    | UTF-8 text, tokenized and full-text indexed for BM25 search |

#### 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.

### 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.

### 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.