Hybrid AI Book Search: How to Understand Meaning, Not Words

In today's world, where the volume of information grows exponentially, the need for intelligent search tools becomes increasingly critical. This is especially relevant for the digital book market, where users often don't know a specific author or title, but instead search for works that match a particular mood or theme. The company red_mad_robot, in collaboration with the Beeline team, developed an innovative AI search capable of understanding the meaning of a query, rather than simply matching keywords. This project, in which philological education proved unexpectedly useful, demonstrates new horizons for the application of artificial intelligence in the field of content search.

The idea for creating such a search emerged from the understanding that many readers formulate their queries quite abstractly: "something atmospheric," "something similar to my favorite novel." Existing search systems, oriented toward exact keyword matching, often prove ineffective in such cases. Therefore, the hypothesis arose for creating a system that could analyze the semantics of a query and suggest relevant books, even if the query itself lacks specific terms.

To implement this idea, a hybrid architecture was developed that combines several approaches. First, vectorization of metadata from half a million books was performed. This made it possible to represent each book as a vector in multidimensional space, reflecting its subject matter, genre, mood, and other characteristics. Second, a large language model (LLM) was trained to process text queries. This model is capable of understanding the meaning of a query, identifying key concepts, and matching them with vectors of books. As a result, the system returns a list of books most relevant to the user's query, even if it is formulated unclearly or metaphorically.

One of the key features of this project is its dual-circuit architecture. The first circuit is responsible for fast search by keywords and metadata. It allows for quick filtering out of books that clearly do not match the query. The second circuit, using LLM, performs deeper semantic analysis and ranks search results based on semantic correspondence. This architecture makes it possible to achieve an optimal balance between search speed and accuracy.

The implementation of AI search by book meaning opens new opportunities for users and book services. Readers receive more relevant search results, which enables them to find interesting books more quickly. Book services, in turn, can improve user experience, increase engagement, and boost sales. Additionally, AI search can be used for personalizing recommendations and creating thematic collections.

This project demonstrates that artificial intelligence can be successfully applied to solve complex problems in the field of content search. The hybrid architecture, combining classical methods and modern LLMs, makes it possible to achieve high accuracy and efficiency. In the future, one can expect further development of similar systems that will be capable of understanding even more complex and nuanced user queries.

In conclusion, the development of hybrid AI search for Beeline's book service is an important step in the advancement of intelligent content search systems. The project demonstrates how the combination of a philological approach and cutting-edge technology can lead to the creation of innovative solutions that improve user experience and open new business opportunities.