The MediaPipe Text Embedder task lets you create a numeric representation of text data to capture its semantic meaning. This functionality is frequently used to compare the semantic similarity of two pieces of text using mathematical comparison techniques such as Cosine Similarity. This task operates on text data with a machine learning (ML) model, and outputs a numeric representation of the text data as a list of high-dimensional feature vectors, also known as embedding vectors, in either floating-point or quantized form. [Open in Web Demos](https://google-ai-edge.github.io/mediapipe-samples-web/#/text/text_embedder)[Edit in StackBlitz](https://stackblitz.com/github/google-ai-edge/mediapipe-samples-web?file=src/tasks/text-embedder.ts)
## Quickstart Install the SDK for your platform, initialize the task with a model, and run inference. ### Web npm`npm install @mediapipe/tasks-text`[Web demo](https://google-ai-edge.github.io/mediapipe-samples-web/#/text/text_embedder)·[Edit in StackBlitz](https://stackblitz.com/github/google-ai-edge/mediapipe-samples-web?file=src/tasks/text-embedder.ts)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_web.md.txt) ```typescript import { FilesetResolver, TextEmbedder } from "@mediapipe/tasks-text"; const MODEL_URL = "https://storage.googleapis.com/mediapipe-models/text_embedder/universal_sentence_encoder/float32/latest/universal_sentence_encoder.tflite"; const textFiles = await FilesetResolver.forTextTasks( "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-text@latest/wasm" ); const textEmbedder = await TextEmbedder.createFromOptions(textFiles, { baseOptions: { modelAssetPath: MODEL_URL }, quantize: true, }); const firstEmbed = textEmbedder.embed("It's a charming and affecting journey.").embeddings[0]; const secondEmbed = textEmbedder.embed("What a great and fantastic trip!").embeddings[0]; const similarity = TextEmbedder.cosineSimilarity(firstEmbed, secondEmbed); ``` ### Android (Kotlin) Gradle`implementation("com.google.mediapipe:tasks-text:latest.release")`[Sample code](https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/text_embedder/android)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_android.md.txt) ```kotlin import com.google.mediapipe.tasks.core.BaseOptions import com.google.mediapipe.tasks.text.textembedder.TextEmbedder import com.google.mediapipe.tasks.text.textembedder.TextEmbedder.TextEmbedderOptions val baseOptions = BaseOptions.builder() .setModelAssetPath("universal_sentence_encoder.tflite") .build() val options = TextEmbedderOptions.builder() .setBaseOptions(baseOptions) .setQuantize(true) .build() val textEmbedder = TextEmbedder.createFromOptions(context, options) val firstEmbed = textEmbedder .embed("It's a charming and affecting journey.") .embeddingResult().embeddings().first() val secondEmbed = textEmbedder .embed("What a great and fantastic trip!") .embeddingResult().embeddings().first() val similarity = TextEmbedder.cosineSimilarity(firstEmbed, secondEmbed) ``` ### Android (Java) Gradle`implementation 'com.google.mediapipe:tasks-text:latest.release'`[Sample code](https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/text_embedder/android)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_android.md.txt) ```java import com.google.mediapipe.tasks.components.containers.Embedding; import com.google.mediapipe.tasks.core.BaseOptions; import com.google.mediapipe.tasks.text.textembedder.TextEmbedder; import com.google.mediapipe.tasks.text.textembedder.TextEmbedder.TextEmbedderOptions; BaseOptions baseOptions = BaseOptions.builder() .setModelAssetPath("universal_sentence_encoder.tflite") .build(); TextEmbedderOptions options = TextEmbedderOptions.builder() .setBaseOptions(baseOptions) .setQuantize(true) .build(); TextEmbedder textEmbedder = TextEmbedder.createFromOptions(context, options); Embedding firstEmbed = textEmbedder .embed("It's a charming and affecting journey.") .embeddingResult().embeddings().get(0); Embedding secondEmbed = textEmbedder .embed("What a great and fantastic trip!") .embeddingResult().embeddings().get(0); double similarity = TextEmbedder.cosineSimilarity(firstEmbed, secondEmbed); ``` ### iOS SwiftPM`https://github.com/google-ai-edge/mediapipe → MediaPipeTasksText`[Package.swift](https://developers.google.com/edge/mediapipe/solutions/setup_ios.md.txt#package_swift)·[Sample code](https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/text_embedder/ios)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_ios.md.txt) ```swift import Foundation import MediaPipeTasksText guard let modelPath = Bundle.main.path( forResource: "universal_sentence_encoder", ofType: "tflite" ) else { return } let options = TextEmbedderOptions() options.baseOptions.modelAssetPath = modelPath options.quantize = true let textEmbedder = try TextEmbedder(options: options) let firstResult = try textEmbedder.embed(text: "It's a charming and affecting journey.") let secondResult = try textEmbedder.embed(text: "What a great and fantastic trip!") if let firstEmbed = firstResult.embeddingResult.embeddings.first, let secondEmbed = secondResult.embeddingResult.embeddings.first { let similarity = try TextEmbedder.cosineSimilarity(embedding1: firstEmbed, embedding2: secondEmbed) } ``` ### Python pip`pip install mediapipe`[Sample code](https://colab.research.google.com/github/google-ai-edge/mediapipe-samples/blob/main/examples/text_embedder/python/text_embedder.ipynb)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_python.md.txt) ```python import mediapipe as mp BaseOptions = mp.tasks.BaseOptions TextEmbedder = mp.tasks.text.TextEmbedder TextEmbedderOptions = mp.tasks.text.TextEmbedderOptions model_path = "universal_sentence_encoder.tflite" options = TextEmbedderOptions(base_options=BaseOptions(model_asset_path=model_path), quantize=True) with TextEmbedder.create_from_options(options) as text_embedder: first_embed = text_embedder.embed("It's a charming and affecting journey.").embeddings[0] second_embed = text_embedder.embed("What a great and fantastic trip!").embeddings[0] similarity = TextEmbedder.cosine_similarity(first_embed, second_embed) ``` ### Flutter pub`flutter pub add mediapipe_text`[Live gallery](https://google.github.io/flutter-mediapipe/)·[Sample code](https://github.com/google/flutter-mediapipe/tree/main/samples/text/text_embedder)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_flutter.md.txt) ```dart import 'package:mediapipe_text/mediapipe_text.dart'; Future compareSentences(String first, String second) async { final embedder = await TextEmbedder.create( TextEmbedderOptions(model: TextModels.universalSentenceEncoder), ); try { final a = await embedder.embed(first); final b = await embedder.embed(second); return TextEmbedder.cosineSimilarity( a.embeddings.first, b.embeddings.first, ); } finally { await embedder.dispose(); } } ``` ## Task details This section describes the capabilities, inputs, outputs, and configuration options of this task. ### Features - **Input text processing** - Supports out-of-graph tokenization for models without in-graph tokenization. - **Embedding similarity computation** - Built-in utility function to compute the [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity) between two feature vectors. - **Quantization** - Supports scalar quantization for the feature vectors. Inputs Text Embedder accepts the following input for processing: - Text (`String`) - Optional formatting context (`TextFormatOptions` on Web, `TextFormatContext` on Android, iOS, and Flutter, or `TextEmbeddingFormatContext` in Python) for supported models like the EmbeddingGemma 300M model. Outputs The Text Embedder outputs a list of embeddings consisting of: - Embedding: the feature vector itself, either in floating-point form or scalar-quantized. - Head index: the index for the head that produced this embedding. - Head name (optional): the name of the head that produced this embedding. ### Configuration options This task has the following configuration options: ### Web | Option Name | Description | Value Range | Default Value | |---|---|---|---| | `l2Normalize` | Whether to normalize the returned feature vector with L2 norm. Use only if the model does not already include a built-in `L2_NORMALIZATION` op. | `Boolean` | `false` | | `quantize` | Whether to scalar-quantize the returned embedding to bytes (assumes unit-norm vectors in `[-1.0, 1.0]`; pair with `l2Normalize` if needed). | `Boolean` | `false` | ### Android | Option Name | Description | Value Range | Default Value | |---|---|---|---| | `l2Normalize` | Whether to normalize the returned feature vector with L2 norm. Use only if the model does not already include a built-in `L2_NORMALIZATION` op. | `Boolean` | `false` | | `quantize` | Whether to scalar-quantize the returned embedding to bytes (assumes unit-norm vectors in `[-1.0, 1.0]`; pair with `l2Normalize` if needed). | `Boolean` | `false` | ### iOS | Option Name | Description | Value Range | Default Value | |---|---|---|---| | `l2Normalize` | Whether to normalize the returned feature vector with L2 norm. Use only if the model does not already include a built-in `L2_NORMALIZATION` op. | `Boolean` | `false` | | `quantize` | Whether to scalar-quantize the returned embedding to bytes (assumes unit-norm vectors in `[-1.0, 1.0]`; pair with `l2Normalize` if needed). | `Boolean` | `false` | ### Python | Option Name | Description | Value Range | Default Value | |---|---|---|---| | `l2_normalize` | Whether to normalize the returned feature vector with L2 norm. Use only if the model does not already include a built-in `L2_NORMALIZATION` op. | `Boolean` | `False` | | `quantize` | Whether to scalar-quantize the returned embedding to bytes (assumes unit-norm vectors in `[-1.0, 1.0]`; pair with `l2_normalize` if needed). | `Boolean` | `False` | ### Flutter | Option Name | Description | Value Range | Default Value | |---|---|---|---| | `l2Normalize` | Whether to normalize the returned feature vector with L2 norm. Use only if the model does not already include a built-in `L2_NORMALIZATION` op. | `bool` | `false` | | `quantize` | Whether to scalar-quantize the returned embedding to bytes (assumes unit-norm vectors in `[-1.0, 1.0]`; pair with `l2Normalize` if needed). | `bool` | `false` | ### Task output Each `TextEmbedderResult` returns an `embeddings` list, and `cosineSimilarity` (`cosine_similarity`) computes semantic similarity between two embeddings in `[-1.0, 1.0]`: | Field | Type / Range | Description | |---|---|---| | `floatEmbedding` | `Float32Array` / `float[]` | Dense float semantic vector (`embedding` in Python; L2-normalized when `l2Normalize` is enabled; in Dart, a `Float32List?` that is `null` when `quantize` is enabled). | | `quantizedEmbedding` | `Uint8Array` / `byte[]` | Scalar-quantized 8-bit vector when `quantize` is enabled (in Dart, a `Uint8List?` holding signed 8-bit values). | | `headIndex` | `int` | Zero-based index of the embedding head (`head_index` in Python). | | `headName` | `string` | Optional name of the embedding head (`head_name` in Python). | #### Example `TextEmbedderResult` payload The following example shows the dense float embedding vector returned for an input string: ```javascript { embeddings: [ { headIndex: 0, floatEmbedding: [0.2345, 0.1234, -0.0891, 0.4102, 0.6789 /* ... */] } ] } ``` ## Models  The Text Embedder supports the smaller Universal Sentence Encoder model and the more capable EmbeddingGemma 300M model for advanced use cases. ### EmbeddingGemma 300M model The TextEmbedder SDK supports the [embeddinggemma-300m](https://huggingface.co/litert-community/embeddinggemma-300m). The provided task file uses a maximum sequence length of 512 tokens. This model handles input differently based on the task type and context format you provide. For the query formats, the template follows `task: | query: `, and for document formats, it follows `title: