Class: TextEmbeddingsModule

Defined in: modules/natural_language_processing/TextEmbeddingsModule.ts:19

Module for managing a Text Embeddings model instance.

Extends

• BaseModule

Properties

generateFromFrame()

generateFromFrame: (frameData, ...args) => any

Defined in: modules/BaseModule.ts:53

Process a camera frame directly for real-time inference.

This method is bound to a native JSI function after calling load(), making it worklet-compatible and safe to call from VisionCamera's frame processor thread.

Performance characteristics:

• Zero-copy path: When using frame.getNativeBuffer() from VisionCamera v5, frame data is accessed directly without copying (fastest, recommended).
• Copy path: When using frame.toArrayBuffer(), pixel data is copied from native to JS, then accessed from native code (slower, fallback).

Usage with VisionCamera:

const frameOutput = useFrameOutput({
  pixelFormat: 'rgb',
  onFrame(frame) {
    'worklet';
    // Zero-copy approach (recommended)
    const nativeBuffer = frame.getNativeBuffer();
    const result = model.generateFromFrame(
      { nativeBuffer: nativeBuffer.pointer, width: frame.width, height: frame.height },
      ...args
    );
    nativeBuffer.release();
    frame.dispose();
  }
});

Parameters

frameData

Frame

Frame data object with either nativeBuffer (zero-copy) or data (ArrayBuffer)

args

...any[]

Additional model-specific arguments (e.g., threshold, options)

Returns

any

Model-specific output (e.g., detections, classifications, embeddings)

See

Frame for frame data format details

Inherited from

BaseModule.generateFromFrame

---

nativeModule

nativeModule: any = null

Defined in: modules/BaseModule.ts:16

Internal

Native module instance (JSI Host Object)

Inherited from

BaseModule.nativeModule

Methods

delete()

delete(): void

Defined in: modules/BaseModule.ts:81

Unloads the model from memory and releases native resources.

Always call this method when you're done with a model to prevent memory leaks.

Returns

void

Inherited from

BaseModule.delete

---

forward()

forward(input, role?): Promise<Float32Array<ArrayBufferLike> | EmbeddingResult>

Defined in: modules/natural_language_processing/TextEmbeddingsModule.ts:101

Embed text into a pooled Float32Array, or a per-token EmbeddingResult for multiVector models.

Parameters

input

string

The text to embed.

role?

EmbeddingRole

Optional role ('query' | 'document') for models with asymmetric prompts; prepends the model's prompt for that role.

Returns

Promise<Float32Array<ArrayBufferLike> | EmbeddingResult>

A Float32Array for pooled models, an EmbeddingResult otherwise.

Throws

If the model is not loaded.

---

forwardET()

protected forwardET(inputTensor): Promise<TensorPtr[]>

Defined in: modules/BaseModule.ts:62

Internal

Runs the model's forward method with the given input tensors. It returns the output tensors that mimic the structure of output from ExecuTorch.

Parameters

inputTensor

TensorPtr[]

Array of input tensors.

Returns

Promise<TensorPtr[]>

Array of output tensors.

Inherited from

BaseModule.forwardET

---

getInputShape()

getInputShape(methodName, index): Promise<number[]>

Defined in: modules/BaseModule.ts:72

Gets the input shape for a given method and index.

Parameters

methodName

string

method name

index

number

index of the argument which shape is requested

Returns

Promise<number[]>

The input shape as an array of numbers.

Inherited from

BaseModule.getInputShape

---

fromCustomModel()

static fromCustomModel(modelSource, tokenizerSource, onDownloadProgress?): Promise<TextEmbeddingsModule>

Defined in: modules/natural_language_processing/TextEmbeddingsModule.ts:77

Creates a text embeddings instance with a user-provided model binary. Use this when working with a custom-exported embeddings model. Internally uses 'custom' as the model name. Note that prompts, multi-vector output, and skipLists are model-config features and are not configured here.

Parameters

modelSource

ResourceSource

A fetchable resource pointing to the model binary.

tokenizerSource

ResourceSource

A fetchable resource pointing to the tokenizer file.

onDownloadProgress?

(progress) => void

Optional callback to monitor download progress, receiving a value between 0 and 1.

Returns

Promise<TextEmbeddingsModule>

A Promise resolving to a TextEmbeddingsModule instance.

---

fromModelName()

static fromModelName(namedSources, onDownloadProgress?): Promise<TextEmbeddingsModule>

Defined in: modules/natural_language_processing/TextEmbeddingsModule.ts:42

Creates a text embeddings instance for a built-in model.

Parameters

namedSources

TextEmbeddingsModel

An object specifying the model name, model source, tokenizer source, and optional prompts / multiVector / skipListIds.

onDownloadProgress?

(progress) => void

Optional callback to monitor download progress, receiving a value between 0 and 1.

Returns

Promise<TextEmbeddingsModule>

A Promise resolving to a TextEmbeddingsModule instance.