{
  "id": "vector",
  "title": "Vector",
  "url": "https://redis.io/docs/latest/develop/ai/redisvl/0.10.0/api/vector/",
  "summary": "",
  "content": "\n\nThe 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.\n\n## Vector\n\n### `class Vector(*, vector, field_name, dtype='float32', weight=1.0)`\n\nSimple object containing the necessary arguments to perform a multi vector query.\n\nCreate a new model by parsing and validating input data from keyword arguments.\n\nRaises [ValidationError][pydantic_core.ValidationError] if the input data cannot be\nvalidated to form a valid model.\n\nself is explicitly positional-only to allow self as a field name.\n\n* **Parameters:**\n  * **vector** (*List* *[* *float* *]*  *|* *bytes*)\n  * **field_name** (*str*)\n  * **dtype** (*str*)\n  * **weight** (*float*)\n\n#### `model_config: ClassVar[ConfigDict] = {}`\n\nConfiguration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].\n",
  "tags": [],
  "last_updated": "2026-04-01T08:10:08-05:00"
}