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

# Neum Search Result

> Data interface for `Neum Search`

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`.

## Usage

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