Implementing RAG (Retrieval-Augmented Generation) in Laravel

RAG solves hallucination by grounding AI in your data.

Steps

1. Store documents as embeddings (OpenAI or local models)
2. Use vector DB (Pinecone, Weaviate, pgvector)
3. Retrieve similar chunks → feed to LLM

Example with pgvector & Laravel:

DB::table('documents_embeddings')
                    ->selectRaw('id, embedding <=> ? as distance', [$queryEmbedding])
                    ->orderBy('distance')
                    ->limit(5)
                    ->get();

We implemented RAG for knowledge-base chat in multiple enterprise projects.