Kuzu V0 136 Info

import kuzu # Initialize or open the database on disk db = kuzu.Database("./analytics_graph") conn = kuzu.Connection(db) # Create a Node Table for Users conn.execute("CREATE NODE TABLE User(id INT64, name STRING, age INT64, PRIMARY KEY (id))") # Create a Relationship Table for Follows conn.execute("CREATE REL TABLE Follows(FROM User TO User)") # Insert sample data using Cypher conn.execute("CREATE (:User id: 1, name: 'Alice', age: 30)") conn.execute("CREATE (:User id: 2, name: 'Bob', age: 25)") conn.execute("CREATE (:User id: 3, name: 'Charlie', age: 35)") # Establish relationships conn.execute("MATCH (a:User id: 1), (b:User id: 2) CREATE (a)-[:Follows]->(b)") conn.execute("MATCH (b:User id: 2), (c:User id: 3) CREATE (b)-[:Follows]->(c)") # Run an analytical 2-hop traversal query result = conn.execute( "MATCH (a:User)-[:Follows]->(b:User)-[:Follows]->(c:User) " "RETURN a.name AS Starter, c.name AS Target" ) while result.has_next(): row = result.get_next() print(f"row[0] is connected to row[1] via a 2-hop path.") Use code with caution. Interoperating with Pandas and Arrow

Because Kùzu is embedded and highly performant, it excels in domains where deploying a massive server-based graph database like Neo4j is overkill or structurally impossible. 1. Graph Retrieval-Augmented Generation (GraphRAG) kuzu v0 136

: Kùzu utilizes "worst-case optimal joins" (WCOJ) to handle dense, cyclic graph structures with high efficiency. Enhancements in Recent Versions import kuzu # Initialize or open the database

Unlike traditional transactional graph databases designed for point lookups (OLTP), Kùzu is purpose-built for online analytical processing (OLAP) on large-scale graphs. Columnar Storage Engine age: 30)") conn.execute("CREATE (:User id: 2