Built an AI-Powered Spring Boot Log Analyzer Using RAG + Ollama

A developer built an AI-powered log analysis platform for Spring Boot applications that uses retrieval-augmented generation (RAG) with Ollama models to parse logs, detect exceptions, and explain root causes. The system, available at loganalyzer.xyz, combines Spring Boot, nomic-embed-text embeddings, and PostgreSQL with pgvector to retrieve similar incidents from a vector database. The project aims to improve debugging by analyzing stack traces and providing LLM-generated explanations for complex Java errors.

I've been working on a log analysis platform that helps debug Spring Boot applications by analyzing logs and stack traces using RAG. https://loganalyzer.xyz/ https://loganalyzer.xyz/ Tech stack: Spring Boot Ollama Qwen/Llama nomic-embed-text embeddings PostgreSQL + pgvector It can parse logs, detect exceptions, retrieve similar incidents from a vector database, and explain potential root causes using an LLM. I'm looking for feedback on the architecture and approach. What would you improve for root-cause analysis of complex Java stack traces? Demo: https://loganalyzer.xyz/ https://loganalyzer.xyz/