The NeumVector is the object used to organize data extracted for a given data source. It is analogous to similar constructs (i.e. Document) used in frameworks like Langchain and LlamaIndex.

The goal of this interface is to abstract three properties:

  • id (str): This is a unique identifier for a given vector. The id is constructed throughout the pre-processing of the data and used as the vector id within the vector database. It is used at synchronization to ensure vectors are not being re-computed and duplicated.
  • score (float): This score is represents the similarity score calculated between the query and the retrieved value.
  • metadata (dict): This value contains the attached metadata for a retrieved vector. This can include values extracted from the data source or loader, as well as any calculated values. Metadata includes the content from the NeumDocument.

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Usage

NeumSearchResult
from neumai.Shared.NeumSearchResult import NeumSearchResult
neum_search_result = NeumSearchResult(
    id = "abc", 
    score = 0.8421,
    metadata = {'text':'Hello', 'createdDate':'2023-01-01'}
)

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Neum Vector