# 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.
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# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
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```
GET https://docs.cosmocloud.io/resources/vector-search/create-a-vector-search-index.md?ask=
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The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
