fast-topic-analysis
A tool for analyzing text against predefined topics using average weight embeddings and cosine similarity.
Creates multiple weighted-average embeddings per topic by clustering similar phrases via agglomerative or HDBSCAN algorithms, capturing semantic variations. Provides preset configurations for high precision, balanced, and performance use cases, and reports per-cluster cohesion plus a global silhouette score. Vector math and clustering are provided by embedding-utils.
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chunk-match
A NodeJS library that semantically chunks text and matches it against a user query using cosine similarity for precise and relevant text retrieval.
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embedding-utils
Vector math, similarity search, ANN indexing, clustering, async pipelines, evaluation metrics, and multi-provider embedding generation -- zero dependencies,...
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extract-topics
Use LDA (Latent Dirichlet Allocation) to extract topics from text