AICL — Luhlaka Lwekunikwa Kwelayisensi Kwelokucuketfwe Kwe-AI
Ilayisensi Yelokucuketfwe Kwe-AI / Luhlaka Lwekunikwa Kwelayisensi Kwelokucuketfwe Kwe-AI
Guculani emalungelo lamenyetelwe abe kunikwa kwelayisensi lokusebentako: intengo, khokha, cinisekisa, hlola, hoxisa.
01 · Incazelo
I-AICL luhlaka lwekunikwa kwelayisensi kanye netekwenta imisebenti lolwakhelwe etulu kwe-AICR. Lapho i-AICR imemetela loko lokuvunyelwe, i-AICL ichaza indlela imvumo letfolwa ngayo ngempela: emabanga elayisensi kanye netintengo, ema-endpoint entengo kanye enkhokhelo, emathokheni elayisensi lasayinwe, tirekhodi tekusebenta nekuhlolwa, kanye nekuhoxiswa. Yakhelwe kutsi isebente kahle netindlela letisha tekukhokha ngemishini njengemigudvu ye-HTTP 402 kanye netindlela tekukhokha ngekutikhrola, ngenkhokhelo yema-ejenti igcinwa ngekhatsi kwetibhajethi letivunywe ngumuntfu kanye netirekhodi tekuhlolwa.
02 · Inhloso
- Guculani imimemetelo yemalungelo ibe imakethe yelayisensi lesebentako esikhundleni sewebhu lelinani lelilodwa levulekile/lelivaliwe.
- Hlelisisa indlela lehambako emkatsini wemishini: tfola umtsetfo → cela intengo → layisensi → sebentisa → hlola.
- Gcina inkhokhelo yema-ejenti inemikhawulo: tibhajethi, imikhawulo yemvumo, emathokheni labotjelwe kusicelo, tirekhodi tekuhlolwa letigcwele.
- Nika bahlinzeki belokucuketfwe indlela yekutfola inkhokhelo ngale kokuvala ema-sayithi abo ku-AI.
03 · Sikhala
Ikhathalogu yelayisensi — emabanga emaphakheji emalungelo anetintengo nemibandzela
Ema-endpoint entengo / enkhokhelo / ekucinisekiswa / ekuhoxiswa
Emathokheni elayisensi lasayinwe laboshelwe kumcelanisi, sikhala, kanye nesikhatsi
Tirekhodi tekusetjentiswa kanye netindzawo tekuhlolwa
Kuhlangana kwe-HTTP 402 "Kudzingeka Inkhokhelo"
Imitsetfo yekuphepha: kute kuphatfwa kwemininingwane yelikhadi lengakalungiswa yi-AI, imikhawulo yemvumo yemuntfu
04 · Sibonelo lesifundzeka ngemishini
Emalayisensi alesisayithi ngekwaso — /.well-known/aicl.json
{
"version": "0.1",
"publisher": "AGIRight.org",
"licenses": [
{
"id": "public_read_summary",
"rights": ["read", "summarize", "quote"],
"price": { "amount": "0", "currency": "USD" },
"conditions": [
"attribution_required",
"no_model_training",
"no_commercial_redistribution"
]
},
{
"id": "research_rag",
"rights": ["read", "summarize", "rag"],
"price": { "amount": "0", "currency": "USD" },
"conditions": [
"attribution_required",
"retention_days_30",
"no_model_training"
]
},
{
"id": "commercial_or_training_license",
"rights": ["training", "commercial_use"],
"requires_contact": true,
"contact": "contact@agiright.org"
}
]
} 05 · Imikhawulo
- Lesisayithi asikwati sango lenkhokhelo; emalayisensi ekuhweba etsintfana ngekutsintfana ku-v0.1.
- I-AICL siphakamiso seliphrothokholi — ayicinisekisi kutsi tinkampani te-AI titakhokha.
- Kuhambisana kwenkhokhelo, tintela, kanye nemtsetfo wetimali kungale kwesikhala saleli drafti.
- Emafomethi ethokheni kanye nemchazo we-endpoint kusahlolwa futsi kungaguculwa.
Sinota semabito
Elucwaningweni lwaseEveMissLab lwangaphambilini, ligama lelifitjiwe AICL nalo libita "AI Ingestion & Capability Layer" — luhlaka lwekwakhiwa lwendlela tinhlelo te-AI letifundza nekushayela ngayo ema-website (imicabango yemanifesti, khophasi, buchule, bulawulo). Kulesisayithi, i-AICL ibhekisela Ku-Luhlaka Lwekunikwa Kwelayisensi Kwelokucuketfwe Kwe-AI ngaphandle kwalapho kushiwo ngalokunye ngalokucacile; umsebenti weluhlaka lwekungena ushicilelwe njengencwadzi lemhlophe lehlukene.