Happy querying, and may your traversals be shallow and your joins deep.
Use Kùzu's vectorized query processor to perform a similarity search while filtering results via keyword matches.
: For implementation details beyond the paper, the official Kùzu Documentation provides the full technical guide for the current versions. Core Technologies Highlighted in the Paper kuzu v0 136 full
is the primary source for the most recent versioning and documentation. installation steps for Kùzu? Kùzu, an extremely fast embedded graph database
This article explores everything you need to know about , with a comprehensive guide to its features, benefits, and how you can get started. Happy querying, and may your traversals be shallow
query = """ MATCH (p:Person)-[k:KNOWS]->(friend:Person) WHERE p.age > 30 RETURN p.name AS source, friend.name AS target, k.since ORDER BY k.since DESC; """
: Added official support for Swift API , Azure storage , and a dedicated LLM extension to facilitate knowledge graph creation for AI. Core Technologies Highlighted in the Paper is the
# Search for a keyword search_res = conn.execute(""" MATCH (p:Person) WHERE p.bio MATCH_TEXT 'graph' RETURN p.name, p.city; """).fetchall() print(search_res)
Kuzu is versatile, but it truly shines in scenarios requiring deep, relationship-driven analysis within the application itself.