Full-Text Search (FTS) in Couchbase is facilitated by the Bleve engine, an open-source text indexing and search library written in Go, which enables the examination and indexing of textual content within JSON documents. Couchbase's FTS engine supports a distributed system architecture that allows data partitioning across multiple nodes in a cluster, enhancing its capability to handle large-scale searches by scattering requests and gathering responses from various nodes. The engine uses an inverted index to link tokens generated from text to documents, improving search query efficiency. Key components of the text analysis process include tokenizers, character filters, and token filters, which work together to break down and refine raw text into tokens suitable for indexing. Users can configure custom analyzers by selecting and ordering these components to tailor the indexing process to specific needs, and a text analysis playground is available for testing stock and custom analyzers. Couchbase also provides guidelines on best practices for effectively utilizing its FTS capabilities.