How 01-keshav/log-analyser works
Log Analyser is a local log-ingestion, analysis, and dashboard application. Users ingest files or text, or collect Windows events; the backend parses and enriches records, stores them, and serves filtered logs, summaries, analytics, and AI-assisted troubleshooting to the dashboard. The README documents a broad analytics and AI capability set; sampled backend excerpts show storage, parsing, and parts of the analytics and Gemini integration, but not every route’s implementation or the collector’s scheduling wiring.
Main entry points: main.py API & orchestration · app.js Dashboard logic · logs-store.json Log store · log_parser.py Format parser
Components 14
Ingestion & Parsing
- Format parser
log_parser.py - MITRE enrichment
mitre_mapping.py - Windows collector
windows_log_collector.py
Analytics & AI
- Log analytics
main.py - Root-cause analysis
main.py - Recommendations
main.py - AI chat & tools
main.py
Backend & Storage
- API & orchestration
main.py - Log store
logs-store.json
Dashboard
- Web dashboard
index.html - Dashboard logic
app.js
Other components
- Engineer
- Gemini API
- Windows event logs
Connections 16
- Engineer → Web dashboard: uses
- Web dashboard → Dashboard logic: loads
- Dashboard logic → API & orchestration: requests API
- API & orchestration → Format parser: parses input
- Format parser → MITRE enrichment: looks up events
- API & orchestration → Log store: loads and saves
- API & orchestration → Log analytics: computes metrics
- API & orchestration → Root-cause analysis: analyzes errors
- API & orchestration → Recommendations: generates guidance
- API & orchestration → AI chat & tools: handles chat
- AI chat & tools → Gemini API: sends prompts
- Windows collector → Windows event logs: fetches events
- API & orchestration → Windows collector: collects logs
- Log analytics → Log store: reads logs
- Root-cause analysis → Log store: reads logs
- Dashboard logic → Gemini API: configures key