RuVector Graph Examples
Graph database features including Cypher queries, distributed clustering, and hybrid search.
Examples
| File | Description |
|---|---|
basic_graph.rs |
Graph creation and traversal |
cypher_queries.rs |
Cypher query language examples |
distributed_cluster.rs |
Multi-node graph clustering |
hybrid_search.rs |
Combined vector + graph search |
Quick Start
cargo run --example basic_graph
cargo run --example cypher_queries
Basic Graph Operations
use ruvector_graph::{Graph, Node, Edge};
let mut graph = Graph::new();
// Add nodes with embeddings
let n1 = graph.add_node(Node {
id: "user:1".to_string(),
embedding: vec![0.1; 128],
properties: json!({"name": "Alice"}),
});
let n2 = graph.add_node(Node {
id: "user:2".to_string(),
embedding: vec![0.2; 128],
properties: json!({"name": "Bob"}),
});
// Create relationship
graph.add_edge(Edge {
from: n1,
to: n2,
relation: "KNOWS".to_string(),
weight: 0.95,
});
Cypher Queries
// Find connected nodes
let query = "MATCH (a:User)-[:KNOWS]->(b:User) RETURN b";
let results = graph.cypher(query)?;
// Pattern matching with vector similarity
let query = "
MATCH (u:User)
WHERE vector_similarity(u.embedding, $query_vec) > 0.8
RETURN u
";
let results = graph.cypher_with_params(query, params)?;
Distributed Clustering
use ruvector_graph::{DistributedGraph, ClusterConfig};
let config = ClusterConfig {
nodes: vec!["node1:9000", "node2:9000"],
replication_factor: 2,
partitioning: Partitioning::Hash,
};
let cluster = DistributedGraph::connect(config)?;
// Data is automatically partitioned
cluster.add_node(node)?;
// Queries are distributed
let results = cluster.query("MATCH (n) RETURN n LIMIT 10")?;
Hybrid Search
Combine vector similarity with graph traversal:
use ruvector_graph::HybridSearch;
let search = HybridSearch::new(graph, vector_index);
// Step 1: Find similar nodes by embedding
// Step 2: Expand via graph relationships
// Step 3: Re-rank by combined score
let results = search.query(HybridQuery {
embedding: query_vec,
relation_filter: vec!["KNOWS", "WORKS_WITH"],
depth: 2,
top_k: 10,
vector_weight: 0.6,
graph_weight: 0.4,
})?;
Graph Algorithms
// PageRank
let scores = graph.pagerank(0.85, 100)?;
// Community detection (Louvain)
let communities = graph.detect_communities()?;
// Shortest path
let path = graph.shortest_path(from, to)?;
// Connected components
let components = graph.connected_components()?;
Use Cases
| Use Case | Query Pattern |
|---|---|
| Social Networks | (user)-[:FOLLOWS]->(user) |
| Knowledge Graphs | (entity)-[:RELATED_TO]->(entity) |
| Recommendations | Vector similarity + collaborative filtering |
| Fraud Detection | Subgraph pattern matching |
| Supply Chain | Path analysis and bottleneck detection |
Performance
- Index Types: B-tree, hash, vector (HNSW)
- Caching: LRU cache for hot subgraphs
- Partitioning: Hash, range, or custom
- Replication: Configurable factor