Use a concept#

Applying a concept to text can be done in one of two ways:

  1. For a single document

  2. For a dataset of documents. See Applying concepts to a Dataset for how to apply a concept to a dataset.

A concept is simply a collection of positive and negative examples. To use a concept, we also need to choose an Embedding. Some embeddings, like gte and sbert will run entirely on device. Other embeddings like openai or cohere require an API key and will make a request to retrieve embeddings. See Embeddings for details on embeddings.

The quality of a concept depends on the chosen embedding. See Concept Metrics for details on understanding the quality of a concept with an embedding.

From the UI#

To use a concept from the UI, click the concept from the Navigation panel, which will open the concept preview page.

From here, you can copy paste text and click “Compute” to compute the concept score for chunks of the given text. The sections of text where Lilac thinks text is related to your concept will be highlighted blue.

From Python#

To use a concept from python, we must create a ConceptSignal. In this example, we’ll use the gte-small embedding which runs entirely on device.

import lilac as ll

signal = ll.signals.ConceptSignal(

# Signals take an iterable of inputs, and return a list of items that match the shape of the input.
result = list(signal.compute(['This product is amazing, thank you!']))



[[{'__value__': {'start': 0, 'end': 35}, 'score': 0.9302721936202625}]]

The resulting output returns a list of spans (start and end positions of the input text) with the resulting concept score.