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1. Clustering Unstructured Text with LLM Embeddings and HDBSCAN

   This article demonstrates how to construct a text clustering pipeline by combining large language model embeddings with HDBSCAN, a density-based algorithm. By transforming raw, unstructured text into semantic mathematical representations, you can automatically discover hidden topics in unlabeled datasets without requiring prior labels.
   
   Key stages of the process include:
   * Generating text embeddings using pre-trained sentence-transformers models
   * Reducing embedding dimensionality with UMAP to prepare data for clustering
   * Applying HDBSCAN to identify clusters and visualize results through scatterplots

   2026-06-24 Tags: text clustering, llm embeddings, sentence transformers, umap, hdbscan, dimensionality reduction, unsupervised learning by klotz
2. Build Semantic Search with LLM Embeddings

   Learn how to build a simple semantic search engine using sentence embeddings and nearest neighbors, focusing on the limitations of keyword-based search and leveraging large language models for semantic understanding.

   2026-03-05 Tags: semantic search, llm embeddings, sentence transformers, nearest neighbors, natural language processing, machine learning, search engine, text embeddings, cosine similarity, retrieval augmented generation by klotz
3. LLM Embeddings vs. TF-IDF vs. Bag of Words: Which Works Better in Scikit-learn?

   This article compares the performance of LLM embeddings, TF-IDF, and Bag of Words for text vectorization and information retrieval tasks using scikit-learn. It provides a practical comparison with code examples and discusses the strengths and weaknesses of each approach.

   2026-02-23 Tags: llm embeddings, tf-idf, bag of words, text vectorization, information retrieval, scikit-learn, text similarity, semantic search by klotz
4. Document Clustering with LLM Embeddings in scikit-learn

   This tutorial demonstrates how to perform document clustering using LLM embeddings with scikit-learn. It covers generating embeddings with Sentence Transformers, reducing dimensionality with PCA, and applying KMeans clustering to group similar documents.

   2026-02-11 Tags: document clustering, llm embeddings, sentence transformers, scikit-learn, pca, kmeans, dimensionality reduction, natural language processing, nlp by klotz

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