https://github.com/markiskorova/ai-ops-assistant
🛠️ AI Ops Assistant — A Go-based backend system for automated log summarization, ticket triage, and changelog generation. Built with GraphQL, JWT auth, PostgreSQL, and a worker queue architecture for scalable operations.
https://github.com/markiskorova/ai-ops-assistant
ai-ops docker go graphql internal-tools openai platform-engineering productivity
Last synced: 4 months ago
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🛠️ AI Ops Assistant — A Go-based backend system for automated log summarization, ticket triage, and changelog generation. Built with GraphQL, JWT auth, PostgreSQL, and a worker queue architecture for scalable operations.
- Host: GitHub
- URL: https://github.com/markiskorova/ai-ops-assistant
- Owner: markiskorova
- License: mit
- Created: 2025-06-06T23:28:28.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-07-15T19:31:06.000Z (11 months ago)
- Last Synced: 2025-09-12T13:10:09.416Z (9 months ago)
- Topics: ai-ops, docker, go, graphql, internal-tools, openai, platform-engineering, productivity
- Language: Go
- Homepage:
- Size: 3.1 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🧠 AI Ops Assistant
AI Ops Assistant is a cloud-native backend project simulating an AI-powered operational triage and summarization platform. Designed with scalability and team productivity in mind, it showcases engineering patterns applicable to internal tooling, data processing, and privacy-conscious automation.
Built 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.
---
## ⚙️ Tech Stack
- **Backend:** Go 1.23, GORM (PostgreSQL), GraphQL (graphql-go)
- **AI Integration:** OpenAI API for summarization
- **Auth:** JWT
- **Infra:** Docker, Docker Compose, Terraform (AWS)
- **DevOps:** GitHub Actions (CI/CD ready)
- **Observability:** Prometheus (metrics), Grafana (dashboards), Alertmanager (alerts)
---
## ✅ Key Features
- 🧾 Summarizes logs using OpenAI's GPT API.
- 🏷️ Classifies tickets with pluggable business logic.
- 🌐 GraphQL API for querying logs, tickets, and changelogs.
- 🔒 JWT-secured authentication and modular user management.
- 🧵 Cleanly separated microservice-style API and worker processes.
- 📊 **Observability with Prometheus & Grafana**:
- API & Worker metrics exported at `/metrics`
- Dashboards for API throughput, latency (P95/P99), error rates
- Worker throughput, failures, and queue depth
- Alert rules for error rates, latency, and backlogs
- 🏗️ Infrastructure-as-Code via Terraform (AWS RDS, ECS, IAM).
- 🐳 Local development via Docker Compose with minimal setup.
---
## 📐 System Design
See the [System Design One-Pager](./docs/AI_Ops_Assistant_System_Design.md) for an architecture breakdown.
---
## 🧪 Run Locally
### Core stack
```bash
docker-compose up --build
```
GraphQL API available at:
```
http://localhost:8080/graphql
```
### With observability stack
```bash
docker-compose -f docker-compose.yml -f docker-compose.obsv.yml up -d --build
```
- Prometheus: [http://localhost:9090](http://localhost:9090)
- Grafana: [http://localhost:3000](http://localhost:3000) (admin / admin)
Provisioned dashboard: **AI Ops — API & Workers**
(Shows API & worker throughput, latency, errors, and queue depth.)
---
## 🛣️ Project Roadmap
See the [Project Plan & Roadmap](./docs/ai_ops_plan_and_roadmap.md) for phased implementation and future plans.
Observability is part of **Phase 3 – Infrastructure & Observability**.
---
## 🧑💻 Author Notes
This 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.
---
MIT License