A step-by-step guide to building an AI-powered content recommendation engine using Laravel, MongoDB Atlas Vector Search, and Hugging Face embeddings. The tutorial covers setting up the Laravel MongoDB package, generating text embeddings with the BAAI/bge-small-en-v1.5 model, storing them in MongoDB, creating a vector search index, and querying for semantically similar posts. It also discusses improvements like user behavior tracking, hybrid keyword+vector search, caching strategies, and real-world use cases in news, e-learning, and e-commerce.
Table of contents
# Prerequisite# Understanding AI Embeddings# Generating embeddings for blog posts# Saving Embeddings in MongoDB# Setting Up MongoDB Vector Search# Building the Recommendation Query# Test the route# Improving the Recommendation System# Performance Considerations# Real-World Use Cases# ConclusionSort: