MATCH (a:User)-[:FOLLOWS*1..3]->(b:User) RETURN a.id, b.id
Furthermore, the significance of Kuzu 0.12.0 extends beyond raw performance; it touches upon the integration of Large Language Models (LLMs) and the AI revolution. In the current era of Retrieval-Augmented Generation (RAG), graph databases have found a new purpose: providing structured context to AI models. Kuzu 0.12.0 is arguably the "best" iteration for this specific use case because of its seamless Python integration. By allowing developers to query graph data directly within a Python environment—interacting with libraries like LangChain and LlamaIndex—Kuzu positions itself as a native tool for the AI developer. It solves the impedance mismatch between unstructured text and structured knowledge graphs, allowing for the construction of sophisticated AI pipelines with minimal friction. kuzu v0 120 best
So, what makes Kuzu v0.120 stand out from the crowd? Here are our top 5 picks: MATCH (a:User)-[:FOLLOWS*1
Ready to dive in? Here are some resources to get you started: By allowing developers to query graph data directly
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