Topical: Topic Modeling Pipeline for Ruby

"Topical brings modern topic modeling to Ruby by orchestrating ClusterKit's clustering algorithms with c-TF-IDF term extraction. Discover meaningful topics in document collections without the complexity of managing multiple libraries. What makes it valuable: Complete pipeline from embeddings to labeled topics in one gem Ruby-native c-TF-IDF implementation for distinctive term extraction Quality metrics (coherence, diversity) for evaluating topic quality Clean integration between Rust clustering performance and Ruby usability Model persistence and configurable logging for production use"

"Perfect for content analysis, customer feedback categorization, research paper organization, and knowledge management - with a Ruby API that feels natural despite the Rust-powered clustering underneath. Advanced usage: Combine with red-candle for LLM-powered topic summaries at the application level, maintaining clean separation of concerns. Links: - GitHub Repository & Documentation - RubyGems - Advanced Examples Built on ClusterKit for clustering and designed to integrate cleanly with the Ruby ML ecosystem. Feedback welcome!"

Topical brings modern topic modeling to Ruby by orchestrating ClusterKit's clustering algorithms with c-TF-IDF term extraction. The gem provides a complete pipeline from embeddings to labeled topics in one package and includes a Ruby-native c-TF-IDF implementation for distinctive term extraction. Built-in quality metrics such as coherence and diversity enable evaluation of topic quality. The project pairs Rust-powered clustering performance with a natural Ruby API, and adds model persistence and configurable logging for production use. Use cases include content analysis, customer feedback categorization, research paper organization, and knowledge management. Advanced usage can combine Topical with red-candle for LLM-powered topic summaries while maintaining separation of concerns.

Read at Rubyflow