The Definitive Guide to RAG in Production

Retrieval-Augmented Generation has matured significantly. This tutorial walks you through building a production-ready RAG system from scratch.

Prerequisites

• Python 3.11+
• PostgreSQL with pgvector
• An embedding model (we'll use OpenAI's text-embedding-3-large)

Step 1: Setting Up Your Vector Store

import pgvector
from sqlalchemy import create_engine
Initialize your vector store
engine = create_engine(DATABASE_URL)

Step 2: Document Chunking Strategy

The key to good RAG is intelligent chunking. We recommend semantic chunking over fixed-size chunks:

from langchain.text_splitter import SemanticChunker
chunker = SemanticChunker(
    embeddings=embeddings,
    breakpoint_threshold_type="percentile"
)

Step 3: Query Pipeline

Build a robust query pipeline with re-ranking and hybrid search for best results.

Best Practices

Always implement hybrid search (semantic + keyword)

Use re-ranking to improve precision

Cache frequent queries

Monitor retrieval quality metrics