AI Stack Blueprint

The Production RAG Pipeline

Chunk, embed, retrieve, and generate — a production-ready retrieval-augmented generation stack.

Goal

Build a complete RAG pipeline using pgvector for vector storage in Postgres, LlamaIndex for document ingestion and chunking, and LangChain for orchestration and LLM integration.

Built For

AI engineers and developers building knowledge-base chatbots, document Q&A systems, or enterprise search over private data.

Est. Cost

$20–$100/mo

Build a production RAG pipeline from scratch in under 2 hours

A.R.C. Scorefresh

81.2/100

Architecture88/100

Reliability80/100

Context72/100

All A.R.C. dimensions score well above the 65-point threshold: Architecture (88) ensures the stack can absorb tool swaps safely, Reliability (80) reflects mature vendor uptime and SLA quality, and Context (72) indicates solid ecosystem momentum. This blueprint is stable for production deployment with no actionable pressure points.