> For the complete documentation index, see [llms.txt](https://docs.aibrow.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.aibrow.ai/api-reference/aibrow/embedding.md).

# Embedding

{% hint style="info" %}
Only available on the <mark style="background-color:green;">extension</mark> & <mark style="background-color:purple;">web</mark> implementations
{% endhint %}

## Static Methods

### <mark style="background-color:red;">static</mark> availability

`static async availability(options) =>`  [`AIModelAvailability`](/api-reference/types/aimodelavailability.md)&#x20;

Get the availability of the on-device language model.

| Options (optional)                                                                                |
| ------------------------------------------------------------------------------------------------- |
| **options** `optional` [`EmbeddingCreateOptions`](/api-reference/types/embeddingcreateoptions.md) |

Returns the availability

### <mark style="background-color:red;">static</mark> compatibility

`static async compatibility(options) =>`  [`AIModelCoreCompatibility`](/api-reference/types/aimodelcorecompatibility.md)&#x20;

Get the availability of the on-device language model.

| Options (optional)                                                                                |
| ------------------------------------------------------------------------------------------------- |
| **options** `optional` [`EmbeddingCreateOptions`](/api-reference/types/embeddingcreateoptions.md) |

Returns the compatibility

### <mark style="background-color:red;">static</mark> create

`static async create(options) =>` [`Embedding`](/api-reference/aibrow/embedding.md)

Creates a new embedding session

| Options (optional)                                                                                |
| ------------------------------------------------------------------------------------------------- |
| **options** `optional` [`EmbeddingCreateOptions`](/api-reference/types/embeddingcreateoptions.md) |

Returns a new Embedding session that can be prompted with the pre-provided configuration

***

## Properties

### gpuEngine

[`AIModelGpuEngine`](/api-reference/types/aimodelgpuengine.md)&#x20;

### dtype

[`AIModelDtype`](/api-reference/types/aimodeldtype.md)&#x20;

### flashAttention

`boolean`

### contextSize

`number`

***

## Methods

### get

`async (input, options) => number[] | number[][]`

Creates a new vector from the provided input

| Input                                                  |
| ------------------------------------------------------ |
| A `string` or `strings[]` to generate a vector(s) from |

| Options (optional)                |
| --------------------------------- |
| **signal** `optional AbortSignal` |

Returns the vector or vectors from the language model

### calculateCosineSimilarity

`(vectorA, vectorB) => number`

Calculates the cosine similarity between two embeddings. Only compare embeddings created by the same model

| vectorA                        |
| ------------------------------ |
| A `number[]` vector to compare |

| vectorB                        |
| ------------------------------ |
| A `number[]` vector to compare |

Returns a value between 0 and 1 representing the similarity. 1 being the most similar

### findSimilar

`(embeddings, target) => any[]`

Finds and sorts similar vectors

| embeddings                                                                          |
| ----------------------------------------------------------------------------------- |
| An `Array<{ id: any, vector: number[] }>` array of objects, each with id and vector |

| target                                          |
| ----------------------------------------------- |
| A `number[]` vector to use as the search target |

Returns a list of ids, sorted by the most similar to the least similar


---

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