Intshayelelo
Ukudityaniswa kwe-Artificial Intelligence (AI) kwizicelo zesoftware kuye kwayiguqula indlela esijongana ngayo nokusombulula iingxaki, ukwenza izigqibo, kunye nokuzenzekelayo. Nangona kunjalo, njengoko usetyenziso oluqhutywa yi-AI lusiya lusiba nzima ngakumbi, imfuno yokhuseleko nolwakhiwo olunokwehla iye yaba yinkxalabo engxamisekileyo. Kule nqaku, siza kuhlolisisa i-intersection ye-AI, ubunjineli besofthiwe, kunye ne-DevOps, ngokugxininisa ngokukodwa ekusebenziseni izixhobo ze-orchestration ezifana ne-DSPy kunye ne-LangChain ukudibanisa i-arhente ye-AI kwiipayipi ze-DevOps.
Imingeni ye-AI-Driven Software Architecture
Usetyenziso lwesoftware oluqhutywa yi-AI lubeka imingeni ekhethekileyo kwiinjineli zesoftware kunye namaqela eDevOps. Eminye yemingeni ephambili ibandakanya:
- Scalability: Iimodeli ze-AI zifuna izibonelelo ezibalulekileyo zokubala, ezinokuthi zikhokelele kwimiba yokulinganisa kwiindawo zemveliso.
- ** Ukhuseleko **: Iimodeli ze-AI zinokuba sesichengeni sokuhlaselwa, njengetyhefu yedatha kunye nokuhlaselwa kwemodeli yokuguqulwa.
- Ukudityaniswa: Iimodeli ze-AI kufuneka zidityaniswe neenkqubo zesoftware ezikhoyo, ezinokuba ngumsebenzi onzima.
- Ukubek'esweni kunye noLondolozo: Iimodeli ze-AI zifuna ukubekwa esweni rhoqo kunye nokugcinwa kwazo ukuqinisekisa ukuba zihlala zichanekile kwaye zithembekile.
Izixhobo zeOkhestra: DSPy kunye neLangChain
Izixhobo ze-Orchestration ezifana ne-DSPy kunye neLangChain zibonelela ngesisombululo kule mingeni ngokuvumela ukuhlanganiswa kwee-arhente ze-AI kwiipayipi ze-DevOps. Ezi zixhobo zibonelela ngesakhelo sokwakha, ukuthunyelwa, kunye nokulawula iimodeli ze-AI, ukuqinisekisa ulwakhiwo olukhuselekileyo kunye nolwakhiwo lwezicelo eziqhutywa yi-AI.
DSPy
I-DSPy sisixhobo se-ochestration esivulelekileyo esakhiwe phezu kwePython. Ibonelela nge-API elula kunye ne-intuitive yokwakha, ukuhambisa, kunye nokulawula iimodeli ze-AI. I-DSPy ixhasa uluhlu olubanzi lwezikhokelo ze-AI, kuquka i-TensorFlow, i-PyTorch, kunye ne-scikit-learn.
LangChain
I-LangChain sesinye isixhobo se-ochestration esivulelekileyo esibonelela ngesakhelo sokwakha, ukuhambisa, nokulawula iimodeli ze-AI. I-LangChain yakhiwe phezu kwe-Rust kwaye inikezela nge-API ephezulu yokusebenza yokudibanisa i-arhente ye-AI kwiipayipi ze-DevOps.
Ukuphumeza uKhuseleko kunye ne-Scalable AI-Driven Software Architecture kunye ne-DSPy
Kweli candelo, siza kuphonononga umzekelo olula wokuphumeza i-software ekhuselekileyo kunye ne-scalable AI-driven architecture kunye ne-DSPy.
Umzekelo: Ukwakha iModeli ye-AI elula nge-DSPy
Makhe siqwalasele umzekelo olula wokwakha imodeli yokufunda umatshini usebenzisa i-DSPy. Siza kusebenzisa ilayibrari ye-scikit-lear ukwakha imodeli yokubuyisela umgca.
yokungenisa dSpy njenge dsp
ukusuka sklearn.linear_model yokungenisa LinearRegression
ukusuka kwi-sklearn.datasets import load_diabetes
# Layisha iseti yedatha yeswekile
uluhlu lwedatha = load_diabetes()
# Yahlula idatha kwiseti yoqeqesho kunye neeseti zovavanyo
X_train, X_test, y_train, y_test = dsp.split_dataset(dataset.data, dataset.target, test_size=0.2)
# Yakha imodeli yohlengahlengiso yomgca
imodeli = ukuRhoxiswa komgca ()
# Qeqesha imodeli kwidatha yoqeqesho
imodeli.fit(X_train, y_train)
# Vavanya imodeli kwidatha yovavanyo
ukuchaneka = imodeli.score(X_test, y_test)
print(f"Ukuchaneka kwemodeli: {chaneka:.2f}")
Le khowudi ibhlokhi ibonisa indlela yokwakha imodeli elula yokufunda umatshini usebenzisa i-DSPy. Silayisha idatha yedatha yeswekile, sahlulahlulwe kwiiseti zoqeqesho kunye novavanyo, sakha imodeli yokubuyisela umgca, siyiqeqeshe kwidatha yoqeqesho, kwaye sivavanye ukuchaneka kwayo kwidatha yovavanyo.
Ukuphumeza uKhuseleko kunye ne-Scalable AI-Driven Software Architecture ngeLangChain
Kweli candelo, siza kuhlolisisa umzekelo olula wokuphumeza i-software ekhuselekileyo kunye ne-scalable AI-driven architecture kunye neLangChain.
Umzekelo: Ukwakha iModeli ye-AI elula ngeLangChain
Makhe siqwalasele umzekelo olula wokwakha imodeli yokufunda ngomatshini usebenzisa iLangChain. Siza kusebenzisa ithala leencwadi leTensorFlow ukwakha imodeli yenethiwekhi ye-neural.
sebenzisa i-langchain njenge-lc;
sebenzisa tensorflow::prelude::*;
// Chaza imodeli yenethiwekhi ye-neural elula
imodeli ye-fn(igalelo: &I-Tensor) -> I-Tensor {
vumela imveliso = input.matmul(&Tensor::new(1, 1));
imveliso
}
// Yenza i-arhente yeLangChain
vumela i-arhente = lc::Agent::new(imodeli);
// Chaza iseti yedatha
vumela iseti yedatha = lc::Iseti yedatha::entsha(vec![
lc::Umzekelo::entsha(vec![1.0, 2.0], vec![3.0]),
lc::Umzekelo::entsha(vec![4.0, 5.0], vec![6.0]),
]);
// Qeqesha imodeli kwiseti yedatha
iarhente.train(&iseti yedatha);
// Vavanya imodeli kwidathasethi
vumela ukuchaneka = agent.evaluate(&dataset);
println!("Ukuchaneka kwemodeli: {}", ukuchaneka);
Le khowudi yekhowudi ibonisa indlela yokwakha imodeli elula yokufunda umatshini usebenzisa iLangChain. Sichaza imodeli yenethiwekhi ye-neural elula, yenza i-agent ye-LangChain, ichaze i-dataset, iqeqeshe imodeli kwi-dataset, kwaye ivavanye ukuchaneka kwayo kwi-dataset.
Top 5-10 Uninzi Imiyalelo / iziqwengana ezisetyenziswayo
Nantsi eyona miyalelo/iziqwengana eziphezulu ezi-5-10 ze-DSPy kunye neLangChain:
DSPy
dsp.split_dataset(data, target, test_size=0.2): Ukwahlula idatasethi kuqeqesho nakwiiseti zovavanyo.dsp.build_model(igama_lemodeli, imodeli_iiparam): Yakha imodeli yokufunda yomatshini isebenzisa igama lomfuziselo elikhankanyiweyo kunye neeparamitha.dsp.train_model(imodeli, idatha): Uqeqesha imodeli yokufunda yomatshini kwidatha ekhankanyiweyo.dsp.valuate_model(imodeli, idatha): Ivavanya ukusebenza komatshini wokufunda kwidatha ekhankanyiweyo.dsp.deploy_model(imodeli, iseva_url): Usebenzisa imodeli yokufunda yomatshini kwi-URL yeseva ekhankanyiweyo.
LangChain
lc::Agent::entsha(imodeli): Yenza i-arhente yeLangChain isebenzisa imodeli ekhankanyiweyo.lc::Iseti yedatha::entsha(imizekelo): Yenza iseti yedatha yeLangChain isebenzisa imizekelo ekhankanyiweyo.lc::train(i-arhente, iseti yedatha): Uqeqesha i-arhente yeLangChain kwiseti yedatha ekhankanyiweyo.lc::vavanya(i-arhente, iseti yedatha): Ivavanya ukusebenza kwe-arhente yeLangChain kwiseti yedatha ekhankanyiweyo.lc::deploy(i-arhente, iseva_url): Usebenzisa i-arhente yeLangChain kwi-URL yomncedisi ekhankanyiweyo.
Umboniso
Nantsi imbonakalo elula yoyilo lwesoftware eqhutywa yi-AI usebenzisa iChart.js:
const ctx = document.getElementById('myChart').getContext('2d');
const itshati = iTshati entsha (ctx, {
uhlobo: 'bar',
idatha: {
iilebhile: ['Ixesha loQeqesho', 'Ixesha lokuNgenisa', 'Ukuchaneka kweModeli'],
uluhlu lwedatha: [{
ileyibheli: 'DSPy',
idatha: [10, 5, 90],
umbala ongasemva: 'rgba(255, 99, 132, 0.2)',
Umbala womda: 'rgba(255, 99, 132, 1)',
umdaUbubanzi: 1
}, {
ilebula: 'LangChain',
idatha: [8, 6, 92],
umbala ongasemva: 'rgba(54, 162, 235, 0.2)',
Umbala womda: 'rgba(54, 162, 235, 1)',
umdaUbubanzi: 1
}]
},
iinketho: {
izikali: {
y: {
qalaAtZero: yinyani
}
}
}
});
Le khowudi ibhlokhi idala itshati yebha elula usebenzisa iChart.js ukujonga ukusebenza kwe-DSPy kunye neLangChain.
Isiphelo
Kule nqaku, sihlolisise i-intersection ye-AI, ubunjineli besofthiwe, kunye ne-DevOps, ngokugxininisa ngokukodwa ekusebenziseni izixhobo ze-orchestration ezifana ne-DSPy kunye neLangChain ukudibanisa i-arhente ye-AI kwiipayipi ze-DevOps. Sibonise indlela yokuphumeza i-architecture ye-software ekhuselekileyo kunye ne-scalable ye-AI esebenzisa i-DSPy kunye neLangChain, kwaye inike umzekelo olula wokwakha imodeli yokufunda umatshini usebenzisa isixhobo ngasinye. Siphinde sanikezela nge-5-10 eyona miyalelo / iziqwengana ezisetyenzisiweyo kwisixhobo ngasinye kunye nokubonwa okulula koyilo lwesoftware eqhutywa yi-AI usebenzisa iChart.js.