AI·rete·RAG

About AI·rete·RAG

ai·rete·rag pairs a Rete rule engine with retrieval-augmented generation: rules decide the what, auditable and repeatable; retrieval explains the why, grounded in your own documents.

In the maker’s words

Hi HN, I built ai·rete·rag because I kept seeing teams put an LLM in charge of decisions that need to be auditable (lending, fraud, clinical triage), then bolt on "guardrails" after the fact. It runs the two in series instead: 1. A pure-Python Rete engine evaluates YAML rules against your facts. The verdict comes only from here. Same facts, same verdict, every time, with salience-based conflict resolution. 2. RAG retrieves passages from your own policy documents, and an LLM writes a plain-English explanation of the decision that was already made, citing those passages. It can't change the verdict. A few things that went further than I expected: - Rules are a graph, not flat lists: nested all/any/not, and rules can assert facts that other rules consume (forward chaining). The decision trace shows the causal chain. - Audit mode records every rule evaluated, including the ones that didn't f…

Where people found it

• Hacker NewsShow HN: AI·rete·RAG – a Rete rule engine decides, RAG explains why46 points11 comments11 days ago