#vector-database

Build a Smart FAQ System in Laravel Using Vector Search

A user types "how do I get my money back" into your FAQ search box. Zero results. Your FAQ has a perfectly clear "Refund Policy" entry — the concepts are identical, the words aren't. That's a keyword search problem, and it's embarrassing in a way that's hard to explain to a product manager. The fix is semantic search: match by meaning instead of exact terms. Laravel's official AI package gives you an embeddings API and a `whereVectorSimilarTo` query builder method that handles this in about 40 lines of real code. This article shows you how to wire it together, from migration to search endpoint.

Building a Vector Database from Scratch vs Using Pinecone/Weaviate

The question isn't whether you need a vector database. If you're working with embeddings — for RAG, semantic search, recommendations, or anything that converts text to vectors — you need somewhere to store and search them. The question is whether you should build that layer yourself or use something that already exists. Most developers approach this wrong. They either reach for a managed service before understanding what it does, or they spend a week building their own before discovering it breaks at 50k vectors. This article covers both paths honestly, with working code for all three approaches.