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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.

read1 min publishedJun 5, 2026

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/ 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/

source & further reading
dev.to — original article Why TPUs Aren't Popular (Even Though They're Cheaper Per Token) When HTML parsing fails: using LLMs to extract messy web data Building a bridge to connect AI coding agents with your UI smoothly
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