You can define a property as a fixed-size list of floats (e.g., dimension 1536 for OpenAI embeddings). Abby Winters Girl Boy - 3.79.94.248
CREATE VECTOR INDEX doc_embedding_index ON Document(embedding); You can use the ~= operator (or specific vector functions) to find the closest matches to a query vector. Forced Sex Videos Hot Apr 2026
This guide covers the most impactful changes in Kuzu 0.12.0, how they affect your current workflows, and how to leverage the new capabilities. The standout feature in Kuzu 0.12.0 is the introduction of native Vector Search capabilities. This allows Kuzu to function as a vector database, enabling similarity search alongside standard graph traversals. This is critical for building Retrieval-Augmented Generation (RAG) applications. How it Works You can now define properties with fixed-size list types (vectors) and create vector indexes on them to perform approximate nearest neighbor (ANN) searches efficiently.
MATCH (d:Document) CALL d.embedding =~ [0.1, 0.2, ..., 0.n] // Your query vector RETURN d.content ORDER BY d._similarity_score DESC LIMIT 5; (Note: Syntax for vector search may vary slightly depending on the specific 0.12.0 patch version; always check the specific docs for the exact function name, often utilizing cosine or euclidean distance functions). Kuzu continues to align closer with standard OpenCypher while adding unique extensions for performance. 1. Projection Lists in RETURN In previous versions, returning a large list of properties required listing them individually. Kuzu 0.12.0 introduces variable projection lists, allowing you to return all properties of a node or a specific subset more easily.
CREATE NODE TABLE Document ( id STRING, content STRING, embedding FLOAT[1536], PRIMARY KEY (id) ); To speed up similarity searches, create an index on the embedding column.
Kuzu is an embedded graph database built for query speed and scalability. Version 0.12.0 represents a significant milestone in the database's evolution, introducing features that bridge the gap between traditional graph querying and modern vector-based AI workloads.