# Create a Vector Search Index
* Head over to the vector search indexes option in the left menu
* Now click on the Create Vector Search Index
* Select environment, db collection and vector search index name.
* In the next step you need to define the mappings
{% hint style="danger" %}
vector is a default field which is only there for the array of floats
{% endhint %}
| Field | Value | Example |
| -------------------- | --------------------------------- | ---------------------------------------- |
| Type | vector \| field | vector |
| Field Name | fields in model | fields with type list of floats in model |
| Number Of Dimensions | number(1 - 4096) | 1023 |
| Similarity Function | cosine \| euclidean \| dotProduct | cosine |
{% hint style="danger" %}
Number Of Dimensions depends on the model which you have used to generate the vector embedings.
{% endhint %}
Once done with filling form then simply click on the create button on the bottom right of the form.
