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*   TensorFlow

TensorFlow
==========

This Embeddings integration runs the embeddings entirely in your browser or Node.js environment, using [TensorFlow.js](https://www.tensorflow.org/js). This means that your data isn't sent to any third party, and you don't need to sign up for any API keys. However, it does require more memory and processing power than the other integrations.

*   npm
*   Yarn
*   pnpm

    npm install @tensorflow/tfjs-core@3.6.0 @tensorflow/tfjs-converter@3.6.0 @tensorflow-models/universal-sentence-encoder@1.3.3 @tensorflow/tfjs-backend-cpu

    yarn add @tensorflow/tfjs-core@3.6.0 @tensorflow/tfjs-converter@3.6.0 @tensorflow-models/universal-sentence-encoder@1.3.3 @tensorflow/tfjs-backend-cpu

    pnpm add @tensorflow/tfjs-core@3.6.0 @tensorflow/tfjs-converter@3.6.0 @tensorflow-models/universal-sentence-encoder@1.3.3 @tensorflow/tfjs-backend-cpu

    import "@tensorflow/tfjs-backend-cpu";import { TensorFlowEmbeddings } from "langchain/embeddings/tensorflow";const embeddings = new TensorFlowEmbeddings();

This example uses the CPU backend, which works in any JS environment. However, you can use any of the backends supported by TensorFlow.js, including GPU and WebAssembly, which will be a lot faster. For Node.js you can use the `@tensorflow/tfjs-node` package, and for the browser you can use the `@tensorflow/tfjs-backend-webgl` package. See the [TensorFlow.js documentation](https://www.tensorflow.org/js/guide/platform_environment) for more information.

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