Document-Based RAG: Better Option for AI Governance

Welcome to the Document RAG Guide: a dual-store (LT + HOT) OpenSearch approach to Retrieval-Augmented Generation that delivers answers that are transparent, deterministic, and governance-ready by design. The repo houses two fully working paths:

• Community / Open-Source Version: a self-contained demo you can run on a laptop or in a pure open-source deployment.
• Enterprise Version: a production-grade variant that layers in ingest pipelines, NetApp FlexCache, SnapMirror, and other operational muscle.

By storing knowledge as documents with enriched metadata (named entities and provenance tags) instead of opaque vectors alone, the agent gains traceability, reduces hallucinations, and meets demanding audit requirements.

Project Purpose

This project was developed to address limitations of traditional vector-centric RAG and to make retrieval explainable and auditable.

• Performance & latency: Benchmarks show that splitting stores isn't about raw speed. The LT/HOT split exists primarily for governance boundaries, retention variations control, and policy asymmetry; retrieval remains lexical-first and observable.
• Transparency and Explainability: Vector embeddings are opaque. Document-based RAG stores explicit entity metadata (explicit_terms, explicit_terms_text) and uses fielded, auditable BM25 queries so you can show why a document matched.

Key objectives include:

• Provide a reference architecture for Document-based RAG with explicit HOT (unstable) and Long-Term (LT) tiers.
• Make promotion from HOT → LT a controlled event that happens only when (1) there is enough positive reinforcement of the data or (2) a trusted human-in-the-loop has verified it.
• Show upgrade paths-from a minimal Python demo to an enterprise pipeline with NetApp enterprise storage.

Benefits Over Vector-Based RAG

Adopting Document-based RAG addresses several key limitations of traditional RAG agents:

• Multi-Step Reasoning: OpenSearch indexing with entity enrichment supports complex, entity-aware queries across related documents.
• Bias Mitigation: Explicit metadata and annotations expose skew and make it easier to correct.
• Improved Compliance and Governance: Provenance fields and auditable clauses enable traceable, regulator-friendly retrieval.
• Risk Management: Deterministic lexical logic and highlights reduce hallucinations and make review straightforward.

Community vs Enterprise: What Changes?

Capability	Community Edition	Enterprise Edition
HOT backing store	tmpfs / RAM-disk on the dev box	NetApp FlexCache for locality; durability via SnapMirror replicas
Governance hooks	Basic provenance tags	Promotion events logged, schema-versioned, and instantly traceable
Latency posture	Latency is secondary to governance boundaries	Latency tuned per SLA, but the split is for governance/policy asymmetry

TL;DR: start with the community guide for laptops and commodity hardware; switch to the enterprise path when you need multi-site, 24×7, and governance at scale. The split exists for control and auditability, not because latency forces it.

Where To Dive Deeper

Document	What it covers
Document Search for Better AI Governance	Vision & governance rationale for Document RAG link
Community Version Guide	Step-by-step setup for the open-source flavour link
Community README	Hands-on commands & scripts link
Enterprise Version Guide	Deep dive on ingest pipelines, FlexCache, SnapMirror link
Enterprise README	Production deployment notes link

Quick Start

# 1. Clone the repo
$ git clone https://github.com/davidvonthenen/document-rag-guide.git

# 2. Pick your path
$ cd document-rag-guide/community_version   # laptop demo
# or
$ cd document-rag-guide/enterprise_version  # prod-ready setup

# 3. Follow the README in that folder

Questions? Open an issue or start a discussion and contributions are welcome!