Vector

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].