We Deleted Our Vector Database (www.turingmind.ai)

A year after launching TuringMind for AI-driven code security analysis, the team faced limitations with existing code review tools like CodeRabbit and Copilot, which often missed critical dependency issues. This realization led them to abandon their initial retrieval-augmented generation (RAG) pipeline, which relied on semantic similarity, prompting them to develop their own structural dependency graph. This new approach categorizes every function, class, and config as a node, defining their relationships as edges, allowing the system to accurately identify dependencies and catch issues that traditional LLMs overlooked. By deleting the vector database and constructing a structural index, the team improved code review accuracy significantly. The graph-based system now queries direct dependencies rather than seeking similar chunks, enabling detection of issues such as configuration drift and circular dependencies. While the new approach enhanced review efficiency, addressing the complexities of indexing across languages and maintaining performance for large codebases remained a challenge. Overall, this shift signifies a substantial advancement in AI code review, illustrating the importance of structural knowledge in software development over mere semantic similarity.