Class: TextEmbeddingsModule

Defined in: modules/natural_language_processing/TextEmbeddingsModule.ts:13

Module for generating text embeddings from input text.

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): Promise<Float32Array<ArrayBufferLike>>

Defined in: modules/natural_language_processing/TextEmbeddingsModule.ts:85

Executes the model's forward pass to generate an embedding for the provided text.

Parameters

input

string

The text string to embed.

Returns

Promise<Float32Array<ArrayBufferLike>>

A Promise resolving to a Float32Array containing the embedding vector.

---

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:65

Creates a text embeddings instance with a user-provided model binary and tokenizer. Use this when working with a custom-exported model that is not one of the built-in presets.

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.

Remarks

The native model contract for this method is not formally defined and may change between releases. Refer to the native source code for the current expected tensor interface.

---

fromModelName()

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

Defined in: modules/natural_language_processing/TextEmbeddingsModule.ts:25

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

Parameters

namedSources

An object specifying which built-in model to load and where to fetch it from.

modelName

TextEmbeddingsModelName

modelSource

ResourceSource

tokenizerSource

ResourceSource

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.