{"id":41347040,"url":"https://github.com/markiskorova/ai-ops-assistant","last_synced_at":"2026-01-23T07:04:43.288Z","repository":{"id":297706417,"uuid":"997663930","full_name":"markiskorova/ai-ops-assistant","owner":"markiskorova","description":"🛠️ AI Ops Assistant — A Go-based backend system for automated log summarization, ticket triage, and changelog generation. 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Designed with scalability and team productivity in mind, it showcases engineering patterns applicable to internal tooling, data processing, and privacy-conscious automation.\n\nBuilt using Go, GraphQL, Docker, and Terraform with secure JWT auth, this system processes logs and tickets asynchronously, summarizes them via OpenAI, and delivers insights via a robust API—making it ideal for platforms focused on data control, governance, and observability.\n\n---\n\n## ⚙️ Tech Stack\n\n- **Backend:** Go 1.23, GORM (PostgreSQL), GraphQL (graphql-go)  \n- **AI Integration:** OpenAI API for summarization  \n- **Auth:** JWT  \n- **Infra:** Docker, Docker Compose, Terraform (AWS)  \n- **DevOps:** GitHub Actions (CI/CD ready)  \n- **Observability:** Prometheus (metrics), Grafana (dashboards), Alertmanager (alerts)  \n\n---\n\n## ✅ Key Features\n\n- 🧾 Summarizes logs using OpenAI's GPT API.  \n- 🏷️ Classifies tickets with pluggable business logic.  \n- 🌐 GraphQL API for querying logs, tickets, and changelogs.  \n- 🔒 JWT-secured authentication and modular user management.  \n- 🧵 Cleanly separated microservice-style API and worker processes.  \n- 📊 **Observability with Prometheus \u0026 Grafana**:\n  - API \u0026 Worker metrics exported at `/metrics`\n  - Dashboards for API throughput, latency (P95/P99), error rates\n  - Worker throughput, failures, and queue depth\n  - Alert rules for error rates, latency, and backlogs  \n- 🏗️ Infrastructure-as-Code via Terraform (AWS RDS, ECS, IAM).  \n- 🐳 Local development via Docker Compose with minimal setup.  \n\n---\n\n## 📐 System Design\n\nSee the [System Design One-Pager](./docs/AI_Ops_Assistant_System_Design.md) for an architecture breakdown.\n\n![Architecture Diagram](./docs/architecture_diagram.png)\n\n---\n\n## 🧪 Run Locally\n\n### Core stack\n```bash\ndocker-compose up --build\n```\nGraphQL API available at:  \n```\nhttp://localhost:8080/graphql\n```\n\n### With observability stack\n```bash\ndocker-compose -f docker-compose.yml -f docker-compose.obsv.yml up -d --build\n```\n- Prometheus: [http://localhost:9090](http://localhost:9090)  \n- Grafana: [http://localhost:3000](http://localhost:3000) (admin / admin)  \n\nProvisioned dashboard: **AI Ops — API \u0026 Workers**  \n(Shows API \u0026 worker throughput, latency, errors, and queue depth.)\n\n---\n\n## 🛣️ Project Roadmap\n\nSee the [Project Plan \u0026 Roadmap](./docs/ai_ops_plan_and_roadmap.md) for phased implementation and future plans.  \nObservability is part of **Phase 3 – Infrastructure \u0026 Observability**.\n\n---\n\n## 🧑‍💻 Author Notes\n\nThis project was created to demonstrate backend leadership and infrastructure fluency aligned with real-world SaaS tooling. Its design prioritizes modularity, secure data handling, observability, and developer productivity.\n\n---\n\nMIT License\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarkiskorova%2Fai-ops-assistant","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmarkiskorova%2Fai-ops-assistant","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarkiskorova%2Fai-ops-assistant/lists"}