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ML-powered email spam detection toolkit with MTA integrations.
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README 전체를 불러오는 중입니다. 아래는 저장된 발췌입니다.
ML-powered email spam detection toolkit with MTA integrations.
Add ML-based spam classification to your existing mail infrastructure — no ML expertise required, no MTA replacement needed, setup in 3 steps.
Your MTA plugin talks to the tobira API over HTTP. No changes to your mail flow.README 전문 보기 ↗ML-powered email spam detection toolkit with MTA integrations.
Add ML-based spam classification to your existing mail infrastructure — no ML expertise required, no MTA replacement needed, setup in 3 steps.
pip install tobira[serving,fasttext]
tobira init # Auto-detect your MTA, generate config
tobira serve # Start API server — done
Your MTA plugin talks to the tobira API over HTTP. No changes to your mail flow.
MTA (Postfix, etc.)
└── MTA Plugin (rspamd / SpamAssassin / Haraka / milter)
└── HTTP POST /predict → tobira API Server
tobira init detects your MTA and generates config. tobira doctor validates your setup.velocitylabo.github.io/tobira — Full documentation:
| Package | Registry | Description |
|---|---|---|
tobira | PyPI | Python toolkit (backends, API server, CLI) |
tobira | npm | JavaScript client for Node.js / Haraka |
| Method | Path | Description |
|---|---|---|
| Docker Compose | docker/ | Full test environment with all MTAs |
| Helm Chart | charts/tobira/ | Kubernetes deployment with HPA/Ingress |
| Plain K8s | k8s/ | Kubernetes manifests without Helm |
MIT