Document-Based RAG: Better Option for AI Governance

Welcome to the Document RAG Guide: a dual-memory (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 reproducible, explainable and auditable.

• Performance & latency: 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

While traditional vector-only RAG excels at semantic "vibes," it often struggles with the precision, auditability, and deterministic grounding required by the enterprise. Transitioning to a lexical-first (BM25 + Vector) architecture provides several critical advantages:

• Deterministic Factual Grounding: BM25 eliminates the "black box" of vector-only ranking by providing traceable, keyword-based evidence for every retrieval, ensuring exact matches for entities like product IDs or legal clauses.
• Audit-Ready Provenance: By utilizing explicit metadata fields (e.g., doc_version, ingested_at_ms), every fact used to ground an LLM response is tied to a verifiable source of truth, satisfying regulatory requirements for data lineage.
• Mitigation of Semantic Drift: Unlike vector embeddings, which can suffer from "hallucinated similarity" in high-dimensional space, lexical search anchors the retrieval in explicit term frequency, preventing contextually irrelevant documents from polluting the LLM prompt.
• Storage-Aware Compliance: Integrating with NetApp storage allows for immutable SnapCenter snapshots and MetroCluster replication, ensuring that the retrieved context is protected by enterprise-grade disaster recovery and point-in-time audit capabilities.
• Operational Explainability: Use of deterministic analyzers and keyword highlights makes it straightforward for human reviewers to understand exactly why the RAG agent selected a specific snippet, reducing the risk of hidden bias in embedding-only systems.

Community vs Enterprise: What Changes?

Capability	Community Edition	Enterprise Edition
HOT backing store	tmpfs / RAM-disk on the dev box	NetApp FlexCache for locality; (Optional) SnapMirror replicas
LT backing store	Rotating Disk, SSD, etc.	NetApp FlexCache for locality; Durability via SnapMirror replicas
Governance hooks	Basic provenance tags	Same as Community Version
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/NetApp/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 deployment

# 3. Follow the README in that folder

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