How to Build a RAG Knowledge-Base Chatbot in Laravel 11 Using pgvector

A developer tutorial on DEV Community walks through building a retrieval-augmented generation (RAG) chatbot using Laravel 11, PostgreSQL, and the pgvector extension — eliminating the need for a third-party vector database SaaS. RAG solves a common chatbot limitation where the AI model lacks knowledge of proprietary data, such as refund policies or API documentation, by storing documents as embeddings and retrieving only relevant chunks per query. The guide recommends RAG only when a knowledge corpus exceeds roughly 20,000 tokens or is frequently updated, suggesting a simpler cached system prompt approach for smaller datasets. The pipeline uses OpenAI's text-embedding-3-small model at 1,536 dimensions, with an HNSW index on PostgreSQL for fast cosine similarity search at scale. Document ingestion is handled via a Laravel Artisan command that batches embedding API calls per file and stores chunked content in a dedicated database table.