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klotz: llm embeddings*

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1. 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
2. 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
3. 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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