RAG Chatbot with Laravel and pgvector
Build a production-grade RAG chatbot using Laravel, PostgreSQL pgvector, and OpenAI embeddings for accurate domain-specific answers.
What is RAG?
Retrieval-Augmented Generation (RAG) combines vector similarity search with LLM generation. Instead of relying solely on the model's training data, RAG retrieves relevant context from your own documents before generating a response.
Setting Up pgvector
pgvector is a PostgreSQL extension that adds vector similarity search. It's perfect for storing OpenAI embeddings and performing fast nearest-neighbor lookups.
The Pipeline
1. Chunk your documents. 2. Generate embeddings via OpenAI. 3. Store in pgvector. 4. At query time, embed the question, find similar chunks, inject into LLM prompt.
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