Text-to-SQL with Knowledge Graphs: Solving Multi-Hop Query Problems

Knowledge graphs provide a more effective solution than vector databases for complex Text-to-SQL queries by mapping database schemas as interconnected structures, enabling Large Language Models (LLMs) to navigate multi-hop relationships and uncover necessary intermediate tables that vector similarity search often overlooks. QueryWeaver, utilizing FalkorDB, leverages this graph-based approach to solve the challenges posed by complex enterprise schemas, such as missing JOIN paths, hallucinated relationships, and incorrect table selections, by storing schema metadata as nodes and relationships. This method addresses common errors in Text-to-SQL conversion by ensuring that all structurally required tables and data pathways are considered, thereby enhancing query accuracy and reliability. The architecture of QueryWeaver includes a knowledge graph, a semantic layer, content awareness, graph traversal, a reasoning buffer, and an autonomous error-correction loop, culminating in a system that not only converts text to SQL but acts as a comprehensive reasoning engine for data.