Keyword search can miss records that describe the same problem using different words. In this tutorial, we will add semantic search to a PHPRunner support-ticket application. A search for ‘customers having access or login problems’ can find tickets about rejected credentials, unauthorized sessions, or account access even when they do not contain the exact search phrase.
The application uses the OpenAI Embeddings API to convert ticket text and each search phrase into vectors. PHPRunner then compares those vectors locally with cosine similarity and returns the 30 closest tickets. This approach is intended for relatively small collections. For tens of thousands of records or more, use a vector database instead of comparing every vector in PHP. The cosine similarity scores in the diagram measure how closely the vectors are related. They are not probabilities or percentages of correctness.
