> ## Documentation Index
> Fetch the complete documentation index at: https://helix-claude-document-return-objects-rxi6v.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# HelixDB Overview

@tags: intro, architecture, helixdb, graph, vector

## TL;DR

* High Performance: Purpose-built storage engine optimized for both graph and vector operations
* Graph-Vector Hybrid: Store both graph and vector data in the same database (less overhead than separate databases)
* Query Language: Designed for traversing and manipulating graph and vector data efficiently
* Type Safety: Explicit type definitions to ensure data integrity
* Developer-Friendly: Simple setup and migration
* Secure: Options for encryption at rest

## Data Model

* **Nodes**: entities with unique identifiers and properties
* **Vectors** – entities with unique identifiers and vector embeddings
* **Edges** - directed relationships between nodes and/or vectors, carrying properties

## Common Use Cases

1. **Social Networks** – friend graphs, content sharing, community detection
2. **Recommendation Engines** – product/content suggestions, “people you may know”, interest matching
3. **Fraud Detection** – transaction pattern recognition, suspicious-relationship analysis, risk scoring
4. **Knowledge Graphs** – semantic search, data integration, ML feature store, research discovery

## When to Use a Graph Database

* Data contains dense, interconnected relationships
* Queries require multi-hop traversals or pattern matching
* Traditional SQL joins become complex or slow
* Hierarchical structures or path finding dominate access patterns

## Benefits Over Traditional Databases

* **Performance**: Faster for relationship-heavy queries
* **Flexibility**: Easier to modify and extend the data model
* **Intuitive**: More natural way to model connected data
* **Scalability**: Better handling of complex relationship patterns