Benchmarking Text-to-Cypher: PIPE-Cypher’s Take on Enterprise Property Graphs

Enterprise property graphs aren't static, and that's precisely where the challenge lies. Each graph comes with its own set of schema structures, domain assumptions, and interaction patterns. Enter PIPE-Cypher, a novel benchmark-generation pipeline tailored for these ever-evolving entities.

The PIPE-Cypher Approach

PIPE-Cypher transforms a live property graph into a balanced NL-to-Cypher benchmark. It uses real data from customer queries and analyst logs, ensuring the benchmarks reflect actual usage scenarios. The process combines schema profiling, execution validation, and a local LLM judge to maintain quality and relevance.

Here's what the benchmarks actually show: zero-shot transfer is notably weak. It's the few-shot controls that shine, demonstrating how schema-specific example banks can significantly aid compatible model families.

Why This Matters

In practice, creating a deployment-relevant Text2Cypher benchmark is no trivial task. The reality is, schemas and values are unique, changing over time. PIPE-Cypher evolves with these changes, ensuring that benchmarks remain applicable and useful.

But why should you care? Well, if you're relying on property graphs to inform business decisions, having a dynamic benchmark means your insights are grounded in reality. It’s not just about the numbers. it’s about ensuring those numbers mean something.

Looking Forward

PIPE-Cypher's approach is a breakthrough for those who need their benchmarks to evolve with their data. With its ability to generate and evaluate 3,000 FinBench/SNB examples and more, it sets a new standard in the field. As enterprise graphs continue to grow and shift, having a benchmark tool that can keep pace is invaluable.

Strip away the marketing, and you get a tool that makes Text2Cypher benchmarking a repeatable, evolving process. It’s not just a static solution. it’s one that grows with you.