<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Python on Phil Wheat</title><link>https://philwheat.net/tags/python/</link><description>Recent content in Python on Phil Wheat</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 13 Oct 2026 09:08:00 -0500</lastBuildDate><atom:link href="https://philwheat.net/tags/python/index.xml" rel="self" type="application/rss+xml"/><item><title>txtai Semantic Search Examples</title><link>https://philwheat.net/links/txtai-semantic-search-examples/</link><pubDate>Tue, 13 Oct 2026 09:08:00 -0500</pubDate><guid>https://philwheat.net/links/txtai-semantic-search-examples/</guid><description>&lt;p&gt;The examples index for txtai, the NeuML embeddings library. The semantic-search section is a notebook gallery: building embeddings indexes from Hugging Face datasets or your own data sources, adding semantic search to Elasticsearch, image+text similarity in one embedding space, custom SQL functions, model explainability, query translation, QA databases, semantic graphs, and BM25 topic modeling. There are parallel sections for LLM/RAG, pipelines, workflows, and model training. A practical catalog if you&amp;rsquo;re evaluating txtai against other RAG stacks.&lt;/p&gt;</description></item></channel></rss>