embeddings

Semantic similarity measures how alike two texts are in meaning using embeddings—numerical vectors that capture text semantics. This Skill converts sentences into 384-dimensional vectors and calculates cosine or Euclidean distance to quantify their relationship objectively.

Yes, this Skill supports batch processing of single or multiple text inputs. Submit multiple sentences or fragments, and it returns embedding vectors and distance metrics for all comparisons simultaneously.

Convert documents into embeddings and compare their cosine distance. Lower distances indicate higher semantic overlap. This Skill automates that process, enabling rapid plagiarism detection or thematic clustering of large document sets.

This Skill is optimized for short texts—sentences and fragments. It uses the all-MiniLM-L6-v2 model, which excels at capturing semantic meaning at the sentence level rather than document-scale analysis.

The Skill implements fault-tolerant loading with lazy initialization of the all-MiniLM-L6-v2 model. If loading fails, error handling ensures graceful degradation rather than complete failure.

This Skill calculates both cosine distance and Euclidean distance between text embeddings, allowing you to choose the metric that best fits your similarity assessment needs.