Adding RAG Knowledge Search to a Laravel App with pgvector

What RAG buys you

Retrieval-augmented generation lets an AI assistant answer questions grounded in your data instead of guessing. In a Laravel SaaS, that means support agents and product Q&A that actually know your catalog and docs.

The moving parts

• Embeddings — convert documents and queries into vectors.
• Vector store — PostgreSQL with the pgvector extension keeps everything in one familiar database.
• Retrieval — find the nearest chunks to a query, then feed them to the model as context.

A clean service layer

Wrap the embedding and retrieval logic in a dedicated service so your controllers stay thin. Queue the embedding work so indexing never blocks a web request, and cache frequent queries to keep latency low.

The payoff: relevant, grounded answers that make the product feel genuinely intelligent.