Cypher Generation vs Tool Invocation: Designing Reliable AI for Graph Databases

Memgraph, a graph database optimized for performance and developer experience, enhances the standard openCypher query language to offer more intuitive and expressive querying capabilities. As natural language interfaces become prevalent due to large language models (LLMs), there's a debate on whether to limit query advancements for LLM compatibility. The article explores two approaches for integrating natural language interfaces with graph databases: direct Cypher generation by LLMs and tool invocation where predefined tools mapped to Cypher logic are used. While direct generation offers flexibility, it poses challenges like bias, performance issues, and safety concerns due to the complexity of prompt engineering and the dynamic nature of databases like Memgraph. Memgraph advocates for a tool-based approach, where LLMs access well-defined tools, providing stability, safety, and control over database interactions. This approach allows developers to optimize queries, maintain performance, and ensure reliable AI systems without the need for continuous retraining. Memgraph's AI Toolkit supports this strategy by offering a collection of tools for building robust AI agents, emphasizing the importance of structure and predictability in dynamic environments.