---
title: "GraphRAG HTAP (Graph + RAG + HTAP)"
---

# GraphRAG HTAP (Graph + RAG + HTAP)

:::caution[Planned]
Graph-RAG and Code-Graph for HeliosDB Full are planned. Code-Graph (`hdb_code`),
Graph-RAG (`WITH CONTEXT`) and the native MCP server are available in HeliosDB Nano
as build-from-source features. The Cypher queries and GraphRAG features on this page
are not reachable in the current HeliosDB Full release; this page describes the planned feature.
:::

GraphRAG HTAP combines graph databases, vector search, and Retrieval-Augmented Generation in a unified platform.

## Overview

HeliosDB GraphRAG HTAP combines:
- **Graph Database**: Native property graph with Cypher and GQL support
- **Vector Database**: Integrated embeddings for semantic search
- **RAG Framework**: Built-in Retrieval-Augmented Generation
- **HTAP Engine**: Hybrid Transactional/Analytical Processing

## Key Benefits

| Benefit | Description |
|---------|-------------|
| **10x Faster** | Outperforms Neo4j + VectorDB combinations |
| **Unified Platform** | Single system vs. fragmented architecture |
| **Operations** | WAL, backup/restore, replication, PITR |
| **ACID Compliant** | Full MVCC with multiple isolation levels |
| **Scalable** | Tested with 10M+ nodes, 100M+ edges |

## Quick Start

### 1. Enable GraphRAG

```sql
CREATE DATABASE knowledge_graph;
SET graphrag_enabled = true;
SET graphrag_embedding_model = 'openai';
```

### 2. Create and Query a Knowledge Graph

```sql
-- Create nodes
MATCH (p:Person {name: 'Alice', age: 30});

-- Create relationships
MATCH (p:Person {name: 'Alice'})-[r:KNOWS]->(p2:Person {name: 'Bob'});

-- Query with Cypher
MATCH (p:Person)-[r:KNOWS]->(friend:Person)
WHERE p.name = 'Alice'
RETURN friend.name;
```

### 3. Combine Graph + Vector Search

```sql
-- Semantic search with graph context
MATCH (p:Person)-[r:works_at]->(org:Organization)
WHERE p.embedding <-> to_vector('software engineer') < 0.5
RETURN p, org;
```

## Use Cases

1. **Knowledge Graphs with LLM Integration**
   - Intelligent chatbots with graph-backed knowledge
   - RAG pipelines with relationship-aware retrieval

2. **Real-Time Analytics**
   - OLTP queries for user interactions
   - OLAP queries for business intelligence
   - Automatic query routing

3. **Graph Machine Learning**
   - Node/edge embeddings with graph structure
   - Community detection and influence analysis
   - Recommendation systems

## Documentation

| Document | Description |
|----------|-------------|
| [USER_GUIDE.md](https://www.heliosdb.com/docs/full/features/graphrag/user_guide/) | Comprehensive user guide |
| [CYPHER_REFERENCE.md](https://www.heliosdb.com/docs/full/features/graphrag/cypher_reference/) | Cypher query language reference |
| [NEO4J_MIGRATION_GUIDE.md](https://www.heliosdb.com/docs/full/features/graphrag/neo4j_migration_guide/) | Migrating from Neo4j |
| [Quick Start](https://www.heliosdb.com/docs/full/quick-starts/features/graphrag_quick_start/) | Fast getting started |

## Related Features

- **Vector Search**: `/docs/features/multimodal-vector/`
- **Full-Text Search**: `/docs/guides/user/FULL_TEXT_SEARCH_TUNING_GUIDE.md`
- **SQL Interface**: `/docs/features/sql-interface/`

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