A step-by-step guide to building a fully local AI code review pipeline using Ollama and open-source LLMs like DeepSeek Coder 6.7B. The setup extracts staged Git diffs via a Node.js engine, sends them to a locally served model, parses structured JSON feedback, and blocks commits on critical issues via a Husky pre-commit hook. A React dashboard visualizes review trends over time. The guide covers model selection, prompt engineering for reliable JSON output, retry logic, atomic JSON logging, and production tips like running Ollama as a systemd service or sharing it over LAN with firewall protection.
Table of contents
How to Set Up a Self-Hosted AI Code Review PipelineTable of ContentsWhy Self-Host Your AI Code Reviewer?Prerequisites and Tech Stack OverviewSetting Up Ollama and Your Local ModelBuilding the Code Review Engine in Node.jsAutomating Reviews with Git HooksBuilding a Simple Review Dashboard in ReactTuning for Better Code Quality ResultsImplementation Checklist and Production TipsWhat You BuiltSort: