Pipeline Doctor

Pipeline Doctor – Your AI-Powered CI/CD Assistant 🩺🚀 🚀 Inspiration As a software engineer, I frequently run into failed GitLab pipelines , often accompanied by cryptic and overwhelming logs . Pinpointing the root cause feels like searching for a needle in a haystack. Debugging becomes even more time-consuming when I have to rely on SREs for support. I wanted to remove this friction and empower developers like myself to self-diagnose and fix CI/CD issues faster and more confidently . That’s how Pipeline Doctor was born—your friendly AI-powered companion that understands, analyses, and heals your pipelines. 🩺 What It Does Pipeline Doctor is an AI-powered CI/CD assistant that: 🔍 Analyses failing pipeline jobs using Gemini 2.0 Flash 🧠 Performs similarity analysis on past failures using MongoDB Atlas Vector Search 🛠️ Suggests AI-generated fixes to .gitlab-ci.yml (when config-related) 🤖 Auto-creates a merge request with: Root cause summary Past similar job context A fix diff patch for .gitlab-ci.yml 📬 Sends this as a GitLab MR comment , or email (if not MR-linked) 🏷️ Labels AI-created MRs with ai-generated and job ID reference It not only saves developer time but also improves long-term pipeline health and debugging capabilities. 🛠 How I Built It Tech Stack : Java 17 + Spring Boot AI Analysis : Used Gemini 2.0 Flash to summarize pipeline failures Generated embeddings for log data Vector Search : Embedded historical job logs in MongoDB Queried for top 3 similar failures using MongoDB Atlas Vector Search Automation : Used GitLab REST APIs to post comments and auto-raise MRs Deployment : Hosted on Google Cloud Run Managed secrets via Google Secret Manager ⚠️ Challenges I Faced First time working with MongoDB Atlas Vector Search —took time to learn the query structure Choosing the best secret management tool took careful evaluation Debugged a major MongoDB authentication error before the demo deadline 🏆 Accomplishments I'm Proud Of 🎯 First solo project combining AI + Vector DB + CI/CD + Cloud 🔧 Created an end-to-end tool that is immediately usable in real developer workflows 🧠 Took a personal pain point and solved it using modern AI tooling 🚀 Got hands-on experience with full-stack DevOps & ML engineering 📚 What I Learned 🧾 Prompt engineering for Gemini 2.0 🔍 Vector similarity search with MongoDB Atlas 🛠️ GitLab APIs for CI/CD automation ☁️ Google Cloud Run and Secret Manager for production deployment 🌱 What's Next for Pipeline Doctor ☁️ Build a cloud-hosted version to increase adoption, as the current version is self-hosted, and the developers need to use their infrastructure to use the application as a CI/CD component 🔘 Add user toggle controls for auto-analysis and auto-fix generation 🔄 Extend support to other CI/CD platforms 🧠 Use RAG (Retrieval-Augmented Generation) to make AI fixes even smarter