TextEmbeddingsModule

TypeScript API implementation of the useTextEmbeddings hook.

API Reference

• For detailed API Reference for TextEmbeddingsModule see: TextEmbeddingsModule API Reference.
• For all text embeddings models available out-of-the-box in React Native ExecuTorch see: Text Embeddings Models.

High Level Overview

import { models, TextEmbeddingsModule } from 'react-native-executorch';
// Creating an instance and loading the model
const textEmbeddingsModule = await TextEmbeddingsModule.fromModelName(
  models.text_embedding.all_minilm_l6_v2()
);

// Running the model
const embedding = await textEmbeddingsModule.forward('Hello World!');

Methods

All methods of TextEmbeddingsModule are explained in details here: TextEmbeddingsModule API Reference

Loading the model

Use the static fromModelName factory method. It accepts a model config object (e.g. ALL_MINILM_L6_V2) containing:

• modelName - Unique name identifying the model.
• modelSource - Location of the used model.
• tokenizerSource - Location of the used tokenizer.
• prompts (optional) - Asymmetric query/document prompts the model is trained with. When present, forward requires a role and prepends the matching prompt.
• multiVector (optional) - When true, forward returns the per-token EmbeddingResult instead of a single pooled Float32Array.
• skipListIds (optional) - Token ids to exclude from late-interaction (MaxSim) scoring.

And an optional onDownloadProgress callback (receiving a value between 0 and 1). It returns a promise resolving to a TextEmbeddingsModule instance.

For more information on loading resources, take a look at loading models page.

Running the model

To run the model, use the forward method. It accepts the text to embed and, for models with asymmetric prompts, an optional role ('query' | 'document'). The method returns a promise resolving to:

• a Float32Array — a single pooled vector — for standard models, or
• an EmbeddingResult with the per-token vectors for multiVector models.