Semantic Content Indexing
When the feature flag LPS-122920 is enabled, the platform can now create text embeddings (numerical representations of input text, also known as vector embeddings) for Object entries. These embeddings are generated using the content from the searchable fields and are meant to capture the meaning and the context of the content. You can select from various available third-party providers and models, such as OpenAI or Hugging Face, to generate these vectors.
Customizable Search
Now that semantic indexing is also supported for Object entries, you can create highly customized searches using Blueprints. Specifically, the Rescore by Text Embedding query element is available for use.This element automatically handles the process of creating vectors from the user’s keywords through the configured provider.
For instance, this capability allows you to combine traditional keyword search with AI-powered vector search techniques to implement hybrid search, now also for Object entries. This combination is quickly becoming the new standard for modern content search and discovery.
Key Benefits:
More Relevant Search Results: Create search experiences that understand the meaning and context of your Object based content or application data and user searches.
The Semantic Search capability is planned to be moved to GA status in early 2026. This timeline is intended to outline Liferay’s general product direction and it is subject to change.