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!