Student Builds Code Review Agent Using a Graph Database Instead of Vector Search

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A computer science student describes frustration with AI code review tools built on retrieval-augmented generation, which missed real dependencies and produced fabricated impact claims. The core insight was that source code has inherent graph structure, with imports, function calls, and commits forming relationships that flat text chunking destroys. The student built an ingestion pipeline that parses repositories into file, function, class, commit, author, and issue nodes linked by typed edges. Using FalkorDB and Cypher, the system traverses call chains to find downstream impact in milliseconds rather than relying on an LLM's guesses. The approach was tested on the Pallets Flask codebase, which contains over 80 files and nearly 1,500 functions.

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