@react-native-rag/executorch

This package provides implementations for the Embeddings and LLM interfaces from react-native-rag, using react-native-executorch to run AI models on-device. This enables you to perform inference directly on the user's device, ensuring privacy and offline capabilities.

Installation

``bash
npm install @react-native-rag/executorch react-native-executorch
`

Usage

$3

This class allows you to use an ExecuTorch-compatible model to generate text embeddings.

`typescript
import { ALL_MINILM_L6_V2, ALL_MINILM_L6_V2_TOKENIZER } from 'react-native-executorch';
import { ExecuTorchEmbeddings } from '@react-native-rag/executorch';

const embeddings = new ExecuTorchEmbeddings({
modelSource: ALL_MINILM_L6_V2,
tokenizerSource: ALL_MINILM_L6_V2_TOKENIZER,
});
`

$3

This class allows you to use an ExecuTorch-compatible language model for text generation.

`typescript
import {
LLAMA3_2_1B,
LLAMA3_2_TOKENIZER,
LLAMA3_2_TOKENIZER_CONFIG,
} from 'react-native-executorch';
import { ExecuTorchLLM } from '@react-native-rag/executorch';

const llm = new ExecuTorchLLM({
modelSource: LLAMA3_2_1B,
tokenizerSource: LLAMA3_2_TOKENIZER,
tokenizerConfigSource: LLAMA3_2_TOKENIZER_CONFIG,
});
`

$3

You can use these classes directly with the useRAG hook:

`typescript
import { useRAG } from 'react-native-rag';
import { ExecuTorchLLM, ExecuTorchEmbeddings } from '@react-native-rag/executorch';
import { MemoryVectorStore } from 'react-native-rag';

const App = () => {
const { isReady, generate } = useRAG({
llm,
vectorStore: new MemoryVectorStore({ embeddings }),
});

// ... your component logic
};
``

React Native RAG is created by Software Mansion

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