The original title is "RAG in Laravel: Embeddings and pgvector for a Knowledge-Base Bot"

Short summary

A practical tutorial for implementing RAG in Laravel 11 using PostgreSQL's pgvector extension and OpenAI embeddings, without needing a vector-database SaaS. Covers document chunking, embedding storage with HNSW indexing, and the full retrieval pipeline with code examples. Includes a useful heuristic: corpora under ~20k tokens can go in a cached system prompt, while larger or frequently updated corpora warrant full RAG.

• •Full RAG pipeline in Laravel 11 with PostgreSQL pgvector — no vector-database SaaS needed
• •Covers chunking strategy, HNSW indexing, batch embedding, and retrieval with code examples
• •Practical rule: under ~20k tokens use cached system prompt; beyond that, build RAG

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