Hybrid RAG with NetApp

Graph + Vector Retrieval with Governance Built In

1. Introduction

This project highlights a Hybrid Retrieval-Augmented Generation (Hybrid RAG) architecture developed and validated by NetApp.

The design combines GraphRAG for explicit truth grounding with vector embeddings for semantic context. In practice, the system extracts entities and relationships (triplets) from source documents into a Knowledge Graph, while also storing chunk embeddings for semantic retrieval. The result is a retrieval architecture that balances governance, explainability, and contextual completeness without relying on opaque vector similarity alone.

This page provides a NetApp-focused overview of the architecture and its enterprise implications. The full open source reference implementation lives here: 👉 https://github.com/NetApp/hybrid-rag-graph-with-ai-governance

2. Why Hybrid RAG

Pure vector RAG is good at "semantic vibes" but weak at answering hard questions like:

• Why did this result match?
• Which facts actually grounded the answer?
• Can we reproduce this result next week?
• Can we defend it to auditors?

Graph-based Hybrid RAG addresses those gaps by grounding retrieval in a Knowledge Graph first, then using vectors as supporting semantic context, not the source of truth.

Key reasons Graph-based Hybrid RAG works:

• Deterministic fact traceability: facts are grounded as explicit entities and relationships rather than inferred only from similarity scores.

• Audit-ready provenance: triplets, nodes, and edges can carry source lineage and metadata so evidence remains inspectable.

• Reduced semantic drift: the graph constrains grounding to extracted relationships, while vectors add supporting narrative context.

• Operational explainability: retrieval can be shown as a subgraph of entities, edges, chunks, and metadata rather than as opaque ANN rankings.

• Contextual completeness: vectors still help retrieve surrounding detail and nuance so answers are not limited to terse graph facts.

This architecture also uses explicit HOT and Long-Term (LT) memory tiers. New or unverified facts can live in HOT memory first, while promotion into LT memory is treated as a controlled event driven by reinforcement or trusted human validation.

3. How NetApp Enhances This Architecture

NetApp extends Graph-based Hybrid RAG with an enterprise storage overlay that makes the design operational at scale.

Key NetApp contributions include:

• Dual-tier graph truth store

  • Long-Term (LT): authoritative Neo4j graph store for curated document, chunk, and entity evidence
  • HOT: low-latency Neo4j working set for new, user-specific, or unvetted graph evidence
  • Vector context store: OpenSearch for semantic chunk retrieval alongside graph grounding

• Long-term durability and cost optimization

  • FabricPool / auto-tiering helps move colder Neo4j and OpenSearch data to lower-cost tiers while keeping retrieval transparent
  • NetApp XCP supports large-scale ingestion from legacy HDFS/NFS sources with integrity verification

• Low-latency HOT operations

  • FlexCache keeps the active HOT graph working set close to inference compute
  • Storage QoS protects HOT query/update paths from noisy-neighbor ingest and rebuild workloads
  • FlexClone enables safe rebuild, migration, and test workflows without destabilizing production HOT memory

• Enterprise resilience and compliance

  • MetroCluster protects Long-Term graph and vector stores with synchronous replication and zero-RPO posture
  • SnapCenter captures application-consistent snapshots of Neo4j and OpenSearch state
  • SnapLock adds WORM protection for environments that require immutable evidence and audit trails

• Safe experimentation

  • FlexClone allows instant, space-efficient copies of indices for testing new analyzers or embedding models without touching production

NetApp's role is not to change how Graph + Vector Hybrid RAG works logically, but to make it reliable, governable, compliant, and operable in real enterprise environments.

4. Visit the GitHub Project for More Details

This page is intentionally concise.

For full technical details, code, and deployment guidance, visit the open source project:

👉 https://github.com/NetApp/hybrid-rag-graph-with-ai-governance

There you'll find:

• A complete Graph + Vector Hybrid RAG reference implementation
• Separate community and enterprise deployment paths
• Supporting guides for Hybrid Graph Search for Better AI Governance, the Community Version, and the Enterprise Version
• Detailed explanations of truth grounding, vector support, and HOT/LT operational workflows

If you're building RAG systems that need to be accurate, explainable, and defensible, that repository is the place to start.