Text search in Memgraph offers an integrated approach to exploring graph data by combining the relevance of text search with the connectivity of graph databases. This integration, powered by the Tantivy search engine and facilitated by the mgcxx and cxx interoperability layers between C++ and Rust, allows users to index and rank text data based on frequency and context rather than exact matches. This approach aids in applications such as recommendation systems, fraud detection, and knowledge graphs, where understanding both the meaning and the relationships within data is crucial. Memgraph's native text search ensures consistency with its ACID transaction model, supporting durability, replication, and multi-tenancy. By utilizing this system, users can efficiently explore their data's connections and meanings, supported by Memgraph's robust infrastructure that maintains performance and reliability across different use cases.