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Integrate text searchers

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Text search allows searching for semantically similar text in a corpus. It works by embedding the search query into a high-dimensional vector representing the semantic meaning of the query, followed by similarity search in a predefined, custom index using ScaNN (Scalable Nearest Neighbors).

As opposed to text classification (e.g. Bert natural language classifier), expanding the number of items that can be recognized doesn't require re-training the entire model. New items can be added simply re-building the index. This also enables working with larger (100k+ items) corpuses.

Use the Task Library TextSearcher API to deploy your custom text searcher into your mobile apps.

Key features of the TextSearcher API

  • Takes a single string as input, performs embedding extraction and nearest-neighbor search in the index.

  • Input text processing, including in-graph or out-of-graph Wordpiece or Sentencepiece tokenizations on input text.


Before using the TextSearcher API, an index needs to be built based on the custom corpus of text to search into. This can be achieved using Model Maker Searcher API by following and adapting the tutorial.

For this you will need:

  • a TFLite text embedder model, such as the Universal Sentence Encoder. For example,
    • the one retrained in this Colab, which is optimized for on-device inference. It takes only 6ms to query a text string on Pixel 6.
    • the quantized one, which is smaller than the above but takes 38ms for each embedding.
  • your corpus of text.

After this step, you should have a standalone TFLite searcher model (e.g. mobilenet_v3_searcher.tflite), which is the original text embedder model with the index attached into the TFLite Model Metadata.

Run inference in Java

Step 1: Import Gradle dependency and other settings

Copy the .tflite searcher model file to the assets directory of the Android module where the model will be run. Specify that the file should not be compressed, and add the TensorFlow Lite library to the module’s build.gradle file:

android {
    // Other settings

    // Specify tflite index file should not be compressed for the app apk
    aaptOptions {
        noCompress "tflite"


dependencies {
    // Other dependencies

    // Import the Task Vision Library dependency (NNAPI is included)
    implementation 'org.tensorflow:tensorflow-lite-task-vision:0.4.0'
    // Import the GPU delegate plugin Library for GPU inference
    implementation 'org.tensorflow:tensorflow-lite-gpu-delegate-plugin:0.4.0'

Step 2: Using the model

// Initialization
TextSearcherOptions options =
TextSearcher textSearcher =
    textSearcher.createFromFileAndOptions(context, modelFile, options);

// Run inference
List<NearestNeighbor> results =;

See the source code and javadoc for more options to configure the TextSearcher.

Run inference in C++

// Initialization
TextSearcherOptions options;
std::unique_ptr<TextSearcher> text_searcher = TextSearcher::CreateFromOptions(options).value();

// Run inference with your input, `input_text`.
const SearchResult result = text_searcher->Search(input_text).value();

See the source code for more options to configure TextSearcher.

Run inference in Python

Step 1: Install TensorFlow Lite Support Pypi package.

You can install the TensorFlow Lite Support Pypi package using the following command:

pip install tflite-support

Step 2: Using the model

from tflite_support.task import text

# Initialization
text_searcher = text.TextSearcher.create_from_file(model_path)

# Run inference
result =

See the source code for more options to configure TextSearcher.

Example results

  metadata: The sun was shining on that day.
  distance: 0.04618
  metadata: It was a sunny day.
  distance: 0.10856
  metadata: The weather was excellent.
  distance: 0.15223
  metadata: The cat is chasing after the mouse.
  distance: 0.34271
  metadata: He was very happy with his newly bought car.
  distance: 0.37703

Try out the simple CLI demo tool for TextSearcher with your own model and test data.