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+Title: Apache Solr Neural Search Tutorial
+category: solr/blogposts
+summary: In this blog post, we show in practice how you can use Apache Solr to index and search vectors and then run a full end-to-end neural search. Written by Ilaria Petreti, Information Retrieval/Machine Learning Engineer at Sease.
+slug: apache-solr-neural-search-tutorial
+URL: blogposts/apache-solr-neural-search-tutorial.html
+save_as: blogposts/apache-solr-neural-search-tutorial.html
+
+# Apache Solr Neural Search Tutorial
+
+In this blog post, we will explore Sease's Neural Search contribution to Apache Solr, providing a detailed description of what is already available through an end-to-end tutorial.
+The purpose of this post is not to go into implementation details but to show in practice how you can use this new Apache Solr feature to index and search vectors and then run a full end-to-end neural search.
+Through practical examples we will see how:
+
+- Apache Solr implementation works, with the new field type and query parser introduced
+- To generate vectors from text and integrate large language models with Apache Solr
+- To run KNN queries (with and without filters) and how to use them for reranking
+
+Learn how you can start using neural search in Apache Solr today - the complete post can be read here: [Apache Solr Neural Search Tutorial](https://sease.io/2023/01/apache-solr-neural-search-tutorial.html)