AI-Powered Semantic Search in Symfony Using PHP and OpenAI Embeddings (www.phpcmsframework.com)

This tutorial walks through adding AI-powered semantic search to a Symfony app using PHP and OpenAI embeddings. It shows how to generate vector embeddings for existing content (articles) with a console command (php bin/console make:command app:generate-embeddings), store them in an Article entity (embedding text column), and expose a /search?q=... endpoint that converts the query to an embedding (OpenAI model text-embedding-3-small), computes cosine similarity against stored vectors, and returns the top 5 semantically closest articles. Prereqs: Symfony 6/7, PHP 8.1+, Composer, MySQL/SQLite, and an OpenAI API key. The example code uses simple curl requests to the OpenAI embeddings endpoint, JSON-encodes vectors in the DB, and implements cosine similarity (dot product / product of norms) in PHP. Significance: this is a practical, end-to-end pattern for upgrading keyword search to semantic retrieval in CMSes, FAQs, recommendations, and admin tooling—enabling results ranked by meaning rather than string matches. Key operational notes: cache or reuse embeddings to avoid repeated API calls, keep your API key in .env.local, and migrate to a vector database (Pinecone, Weaviate, Qdrant) for scale and performance when you have thousands of records. The post emphasizes straightforward integration but also highlights performance and security trade-offs for production deployment.