Kenyelletso
Ho kopanngoa ha Artificial Intelligence (AI) lits'ebetsong tsa software ho fetotse tsela eo re atamelang ho rarolla mathata, ho etsa liqeto le ho iketsetsa lintho. Leha ho le joalo, ha lits'ebetso tse tsamaisoang ke AI li ntse li rarahana le ho feta, tlhoko ea meaho e sireletsehileng le e ka senyehang e fetohile ntho e tšoenyang haholo. Sehloohong sena, re tla hlahloba mateano a litsela tsa AI, boenjiniere ba software, le DevOps, ka khatiso e khethehileng ea ho sebelisa lisebelisoa tsa 'mino oa liletsa tse kang DSPy le LangChain ho kopanya liakhente tsa AI ho liphaephe tsa DevOps.
Liqholotso tsa Moralo oa Software oa AI-Driven
Lisebelisoa tsa software tse tsamaisoang ke AI li hlahisa mathata a ikhethang ho lienjiniere tsa software le lihlopha tsa DevOps. Tse ling tsa mathata a bohlokoa li kenyelletsa:
- Scalability: Mefuta ea AI e hloka lisebelisoa tse kholo tsa khomphutha, tse ka lebisang mathateng a scalability libakeng tsa tlhahiso.
- Tshireletso: Mefuta ea AI e ka ba kotsing ea ho hlaseloa, joalo ka chefo ea data le litlhaselo tsa ho kheloha ha mohlala.
- ** Ho kopanya **: Mehlala ea AI e hloka ho kopanngoa le mekhoa ea software e teng, e ka bang mosebetsi o rarahaneng.
- Tlhokomelo le Tlhokomelo: Mefuta ea AI e hloka tlhahlobo e tsoelang pele le tlhokomelo ho netefatsa hore e lula e nepahetse ebile e tšepahala.
Lisebelisoa tsa Orchestration: DSPy le LangChain
Lisebelisoa tsa 'mino oa liletsa tse kang DSPy le LangChain li fana ka tharollo mathateng ana ka ho nolofalletsa ho kopanngoa ha baemeli ba AI ho liphaephe tsa DevOps. Lisebelisoa tsena li fana ka moralo oa ho aha, ho tsamaisa le ho laola mefuta ea AI, ho netefatsa meralo e sireletsehileng le e ka senyehang bakeng sa lits'ebetso tse tsamaisoang ke AI.
DSPy
DSPy ke sesebelisoa sa 'mino o bulehileng oa mohloli o hahiloeng holim'a Python. E fana ka API e bonolo le e hlakileng ea ho aha, ho tsamaisa le ho laola mefuta ea AI. DSPy e ts'ehetsa mefuta e mengata e fapaneng ea meralo ea AI, ho kenyeletsoa TensorFlow, PyTorch, le scikit-learn.
LangChain
LangChain ke sesebelisoa se seng sa 'mino o bulehileng oa mohloli o fanang ka moralo oa ho haha, ho tsamaisa le ho laola mefuta ea AI. LangChain e hahiloe ka holim'a Rust 'me e fana ka API ea ts'ebetso e phahameng bakeng sa ho kopanya baemeli ba AI ho liphaephe tsa DevOps.
Ho kenya Ts'ebetsong Meaho e Sireletsehileng le e Scalable ea AI-Driven Software ka DSPy
Karolong ena, re tla hlahloba mohlala o bonolo oa ho kenya ts'ebetsong meralo ea software e sireletsehileng le e ka senyehang ea AI ka DSPy.
Mohlala: Ho aha Mohlala o Bonolo oa AI ka DSPy
Ha re nahaneng ka mohlala o bonolo oa ho aha mohlala oa ho ithuta oa mochini o sebelisang DSPy. Re tla sebelisa laeborari ea ho ithuta ka scikit ho aha mofuta oa mola oa tlhophiso.
kenya dspy joalo ka dsp
ho tsoa ho sklearn.linear_model import LinearRegression
ho tsoa ho sklearn.datasets import load_diabetes
# Kenya datha ea lefu la tsoekere
dataset = load_diabetes()
# Arola datha ho li-sete tsa koetliso le liteko
X_train, X_test, y_train, y_test = dsp.split_dataset(dataset.data, dataset.target, test_size=0.2)
# Theha mofuta oa tatellano oa mela
mohlala = LinearRegression()
# Koetlisa mohlala ho data ea koetliso
model.fit(X_train, y_train)
# Lekola mohlala ho data ea liteko
nepahalo = mohlala.score(X_test, y_test)
hatisa(f"Ho nepahala ha mohlala: {ho nepahala:.2f}")
Sebaka sena sa khoutu se bontša mokhoa oa ho haha mohlala o bonolo oa ho ithuta oa mochini o sebelisang DSPy. Re kenya pokello ea lintlha tsa lefu la tsoekere, re e arola ka lihlopha tsa koetliso le liteko, re aha mohlala oa tatellano ea maemo, re e koetlisa ka lintlha tsa koetliso, 'me re lekola ho nepahala ha eona ho data ea liteko.
Ho kenya Ts'ebetsong Sebopeho se Sireletsehileng le se Scalable sa AI-Driven Software ka LangChain
Karolong ena, re tla hlahloba mohlala o bonolo oa ho kenya ts'ebetsong meralo ea software e sireletsehileng le e senyehang ea AI ka LangChain.
Mohlala: Ho aha Mohlala o Bonolo oa AI ka LangChain
Ha re nahaneng ka mohlala o bonolo oa ho haha mohlala oa ho ithuta oa mochini o sebelisang LangChain. Re tla sebelisa laeborari ea TensorFlow ho aha mofuta oa neural network.
sebelisa langchain joalo ka lc;
sebelisa tensorflow::prelude::*;
// Hlalosa mohlala o bonolo oa netweke ea neural
fn mohlala(kenyo: &Tensor) -> Tensor {
tlohela tlhahiso = input.matmul(&Tensor::ncha(1, 1));
tlhahiso
}
// Bopa moemeli oa LangChain
tlohella moemeli = lc::Moemeli::ncha(mohlala);
// Hlalosa pokello ya data
tlohella dataset = lc::Dataset::new(vec![
lc::Mohlala::ncha(vec![1.0, 2.0], vec![3.0]),
lc::Mohlala::ncha(vec![4.0, 5.0], vec![6.0]),
]);
// Koetlisa mohlala ho dataset
agent.train(&dataset);
// Lekola mohlala ho dataset
lumella ho nepahala = moemeli.evaluate(&dataset);
println!("Ho nepahala ha mohlala: {}", ho nepahala);
Sebaka sena sa khoutu se bontša mokhoa oa ho haha mohlala o bonolo oa ho ithuta mochine o sebelisa LangChain. Re hlalosa mokhoa o bonolo oa marang-rang oa neural, ho theha moemeli oa LangChain, ho hlalosa dataset, ho koetlisa mohlala ho dataset, le ho hlahloba ho nepahala ha eona ho dataset.
Top 5-10 Litaelo / Linotsoana tse Sebelisitsoeng ka ho Fetisisa
Mona ke tse 5-10 tse sebelisoang ka ho fetesisa litaelo/likotoana tsa DSPy le LangChain:
DSPy
dsp.split_dataset(data, target, test_size=0.2): E arola pokello ea boitsebiso ka lihlopha tsa koetliso le liteko.dsp.build_model(model_name, model_params): E aha mofuta oa ho ithuta oa mochini o sebelisang lebitso le lipharamitha tse boletsoeng.dsp.train_model(mohlala, data): Koetlisa mokhoa oa ho ithuta oa mochini ka data e boletsoeng.dsp.evaluate_model(mohlala, data): E lekola ts'ebetso ea mofuta oa ho ithuta oa mochini ho data e boletsoeng.dsp.deploy_model(mohlala, seva_url): E sebelisa mochini oa ho ithuta ho URL ea seva e boletsoeng.
LangChain
lc::Moemeli::ncha(mohlala): E etsa moemeli oa LangChain ka mokhoa o boletsoeng.lc::Dataset::ncha(mehlala): E theha pokello ea lintlha tsa LangChain ho sebelisoa mehlala e boletsoeng.lc::train(agent, dataset): Koetlisa moemeli oa LangChain ho dataset e boletsoeng.lc::evaluate(agent, dataset): E lekola ts'ebetso ea moemeli oa LangChain ho dataset e boletsoeng.lc::deploy(agent, server_url): E romela moemeli oa LangChain ho URL ea seva e boletsoeng.
Ho bona pono
Mona ke pono e bonolo ea meralo ea software e tsamaisoang ke AI e sebelisang Chart.js:
const ctx = document.getElementById('myChart').getContext('2d');
const chate = Chate e ncha (ctx, {
mofuta: 'bar',
lintlha: {
mangolo: ['Nako ea Koetliso', 'Nako ea Inference', 'Ho Nepaha ha Mohlala'],
lethathamo la lintlha: [{
label: DSPy
lintlha: [10, 5, 90],
backgroundMmala: 'rgba(255, 99, 132, 0.2)',
moeliMmala: 'rgba(255, 99, 132, 1)',
borderWidth: 1
}, {
label: 'LangChain'
lintlha: [8, 6, 92],
backgroundMmala: 'rgba(54, 162, 235, 0.2)',
moeliMmala: 'rgba(54, 162, 235, 1)',
borderWidth: 1
}]
},
likhetho: {
sekala: {
y: {
startAtZero: 'nete
}
}
}
});
Sebaka sena sa khoutu se etsa chate e bonolo ea bar e sebelisang Chart.js ho bona ka mahlo a kelello ts'ebetso ea DSPy le LangChain.
Qetello
Sehloohong sena, re hlahlobile mateano a litsela tsa AI, boenjiniere ba software, le DevOps, ka khatiso e khethehileng ea ho sebelisa lisebelisoa tsa 'mino oa liletsa tse kang DSPy le LangChain ho kopanya liakhente tsa AI ho liphaephe tsa DevOps. Re ile ra bontša mokhoa oa ho kenya ts'ebetsong mekhoa e sireletsehileng le e senyehang ea lisebelisoa tsa AI tse tsamaisoang ke AI ho sebelisa DSPy le LangChain, 'me ra fana ka mohlala o bonolo oa ho haha mochine oa ho ithuta mochine o sebelisa sesebelisoa ka seng. Hape re fane ka litaelo / li-snippets tse 5-10 tse sebelisoang haholo bakeng sa sesebelisoa ka seng le pono e bonolo ea meralo ea software e tsamaisoang ke AI e sebelisang Chart.js.