LangChain

In any enterprise tech team that claims to be “fully AI-powered,” the bill nobody wants to look at closely is usually not production inference — it’s the evaluation pipelines we run to deceive ourselves. Over the past three years, the biggest, most sacred, and least defensible totem in the enterprise stack has been the “private enterprise knowledge base” — first repackaged as RAG, then upgraded again as GraphRAG with a knowledge-graph label. Every architect, when drawing the final architecture diagram for the CTO, dutifully adds the last box: “Closed-loop evaluation and continuous alignment.”

Building a Local RAG with LangChain + Ollama + pgvector: A Complete Hands-On Walkthrough from 0 to 1 (with uv Dependency Management and an Interview Guide) # This post is a directly actionable document. Follow it top to bottom and you will build a local RAG (retrieval-augmented generation) system from scratch and understand the key concepts and code. All core scripts come with comments to make learning and interview review easier.