# Vector

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  "tableOfContents": {"sections":[{"children":[{"id":"class-vector-vector-fieldname-dtypefloat32-weight10-maxdistance20","title":"class Vector(*, vector, fieldname, dtype='float32', weight=1.0, maxdistance=2.0)"}],"id":"vector","title":"Vector"}]}

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The Vector class in RedisVL is a container that encapsulates a numerical vector, it’s datatype, corresponding index field name, and optional importance weight. It is used when constructing multi-vector queries using the MultiVectorQuery class.

## Vector

### `class Vector(*, vector, field_name, dtype='float32', weight=1.0, max_distance=2.0)`

Simple object containing the necessary arguments to perform a multi vector query.

Args:
vector: The vector values as a list of floats or bytes
field_name: The name of the vector field to search
dtype: The data type of the vector (default: "float32")
weight: The weight for this vector in the combined score (default: 1.0)
max_distance: The maximum distance for vector range search (default: 2.0, range: [0.0, 2.0])

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

* **Parameters:**
  * **vector** (*List* *[* *float* *]*  *|* *bytes*)
  * **field_name** (*str*)
  * **dtype** (*str*)
  * **weight** (*float*)
  * **max_distance** (*float*)

#### `validate_vector()`

If the vector passed in is an array of float convert it to a byte string.

* **Return type:**
  *Self*

#### `model_config: ClassVar[ConfigDict] = {}`

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].