AlloyDB enhances enterprise search with hybrid capabilities, combining text, vector, and keyword searches through SQL queries. A significant advancement is its support for logographical languages like Chinese and Japanese, which traditionally struggle with whitespace-based tokenization in databases. Standard PostgreSQL parsers fail to segment these languages correctly, treating entire sentences as single units. Traditional workarounds like third-party extensions or external preprocessing pipelines present limitations and operational complexity. AlloyDB AI Functions now enable native integration of Gemini models directly within the database for intelligent text parsing. This eliminates data movement, simplifies administration, and leverages large language model intelligence for accurate word segmentation. The solution involves creating a table to store raw content, segmented text, search vectors, and vector embeddings. Batch processing using an in-database stored procedure with array aggregation effectively handles large datasets without memory exhaustion or excessive locking. Choosing between 'simple' and 'english' text search configurations in PostgreSQL depends on the dataset's language composition. Search queries must also be pre-processed using the same AI segmentation logic to ensure accurate matching. Implementing RUM indexes on the search vector further optimizes relevance calculation and query performance. This native integration allows for highly accurate full-text search in logographical languages, overcoming previous limitations.