##Chiyambi

Kuphatikizika kwa Artificial Intelligence (AI) m'mapulogalamu apulogalamu kwasintha momwe timayendera njira zothetsera mavuto, kupanga zisankho, ndi makina. Komabe, momwe ntchito zoyendetsedwa ndi AI zikuchulukirachulukira, kufunikira kwa zomangamanga zotetezeka komanso zowopsa kwakhala vuto lalikulu. M'nkhaniyi, tiwona mphambano ya AI, uinjiniya wamapulogalamu, ndi DevOps, ndikugogomezera kwambiri kugwiritsa ntchito zida zoyimba ngati DSPy ndi LangChain kuphatikiza othandizira a AI mu mapaipi a DevOps.

Zovuta za AI-Driven Software Architecture

Mapulogalamu a mapulogalamu oyendetsedwa ndi AI amabweretsa zovuta kwa akatswiri opanga mapulogalamu ndi magulu a DevOps. Ena mwamavuto akulu ndi awa:

• Scalability: Mitundu ya AI imafunikira zida zowerengera, zomwe zitha kubweretsa zovuta m'malo opanga.
• Chitetezo: Mitundu ya AI imatha kukhala pachiwopsezo chowukiridwa, monga kupha ma data komanso kusokoneza ma model.
• ** Kuphatikiza **: Mitundu ya AI iyenera kuphatikizidwa ndi mapulogalamu omwe alipo kale, omwe angakhale ntchito yovuta.
• Kuwunika ndi Kusamalira: Mitundu ya AI imafunikira kuyang'anira ndi kukonza mosalekeza kuti zitsimikizire kuti zimakhala zolondola komanso zodalirika.

Zida Zoyimba: DSPy ndi LangChain

Zida za orchestration monga DSPy ndi LangChain zimapereka njira yothetsera mavutowa popangitsa kuti ma AI agwirizane ndi mapaipi a DevOps. Zida izi zimapereka dongosolo lomanga, kutumiza, ndikuwongolera mitundu ya AI, kuwonetsetsa kuti mamangidwe otetezeka komanso owopsa a mapulogalamu oyendetsedwa ndi AI.

DSPy

DSPy ndi chida chotsegulira gwero lotseguka chomwe chimamangidwa pamwamba pa Python. Imapereka API yosavuta komanso yodziwika bwino yomanga, kutumiza, ndikuwongolera mitundu ya AI. DSPy imathandizira machitidwe osiyanasiyana a AI, kuphatikiza TensorFlow, PyTorch, ndi scikit-lern.

LangChain

LangChain ndi chida china choyimba chotseguka chomwe chimapereka chimango chomangira, kutumiza, ndikuwongolera mitundu ya AI. LangChain imamangidwa pamwamba pa Rust ndipo imapereka API yogwira ntchito kwambiri pophatikiza othandizira a AI mu mapaipi a DevOps.

Kukhazikitsa Zomangamanga Zotetezedwa ndi Zowopsa za AI-Driven ndi DSPy

Mu gawoli, tiwona chitsanzo chosavuta chokhazikitsa mapulogalamu otetezeka komanso owopsa oyendetsedwa ndi AI ndi DSPy.

Chitsanzo: Kumanga Chitsanzo Chosavuta cha AI ndi DSPy

Tiyeni tione chitsanzo chophweka chomanga makina ophunzirira makina pogwiritsa ntchito DSPy. Tidzagwiritsa ntchito laibulale ya scikit-lern kuti tipange njira yosinthira mzere.

lowetsani dsp ngati dsp
kuchokera ku sklearn.linear_model import LinearRegression
kuchokera ku sklearn.datasets import load_diabetes

# Kwezani deta ya shuga
deta = load_diabetes()

# Gawani nkhokwe mumagulu ophunzitsira ndi kuyesa
X_train, X_test, y_train, y_test = dsp.split_dataset(dataset.data, dataset.target, test_size=0.2)

# Pangani njira yosinthira mizere
chitsanzo = LinearRegression ()

# Phunzitsani chitsanzo pazomwe mukuphunzitsidwa
model.fit(X_train, y_train)

# Yang'anani chitsanzo pazomwe mukuyesa
kulondola = model.score(X_test, y_test)
sindikizani(f"Kulondola kwachitsanzo: {kulondola:.2f}")

Chida ichi chikuwonetsa momwe mungapangire njira yosavuta yophunzirira makina pogwiritsa ntchito DSPy. Timayika mndandanda wazinthu za matenda a shuga, kuwagawa m'magulu ophunzitsira ndi kuyezetsa, kupanga njira yochepetsera pang'onopang'ono, kuwaphunzitsa pazomwe amaphunzitsidwa, ndikuwunika kulondola kwake pazoyeserera.

Kukhazikitsa Zomangamanga Zotetezedwa ndi Zowopsa za AI-Driven ndi LangChain

M'chigawo chino, tiwona chitsanzo chosavuta cha kukhazikitsa mapulogalamu otetezeka komanso owopsa oyendetsedwa ndi AI ndi LangChain.

Chitsanzo: Kumanga Chitsanzo Chosavuta cha AI ndi LangChain

Tiyeni tione chitsanzo chosavuta chomanga makina ophunzirira makina pogwiritsa ntchito LangChain. Tigwiritsa ntchito laibulale ya TensorFlow kupanga mtundu wa neural network.

gwiritsani ntchito langchain ngati lc;
gwiritsani ntchito tensorflow::prelude::*;

// Tanthauzirani mtundu wosavuta wa neural network
fn (zolowera: &Tensor) -> Tensor {
    lolani zotuluka = input.matmul(&Tensor::new(1, 1));
    zotuluka
}

// Pangani wothandizira wa LangChain
lolani wothandizira = lc::Agent::watsopano(chitsanzo);

// Tanthauzirani deta
lolani deta = lc::Dataset::new(vec![
    lc::Chitsanzo::chatsopano(vec![1.0, 2.0], vec![3.0]),
    lc::Chitsanzo::chatsopano(vec![4.0, 5.0], vec![6.0]),
]);

// Phunzitsani chitsanzo pa dataset
agent.train(&dataset);

// Unikani chitsanzo pa dataset
lolani kulondola = agent.evaluate(&dataset);
println!("Kulondola kwachitsanzo: {}", kulondola);

Chida ichi chikuwonetsa momwe mungapangire njira yosavuta yophunzirira makina pogwiritsa ntchito LangChain. Timalongosola chitsanzo chosavuta cha neural network, kupanga wothandizira LangChain, kufotokozera deta, kuphunzitsa chitsanzo pa dataset, ndikuwunika kulondola kwake pa dataset.

Apamwamba 5-10 Omwe Amagwiritsidwa Ntchito Kwambiri / Zidutswa

Nawa malamulo / zidule za 5-10 zomwe zimagwiritsidwa ntchito kwambiri pa DSPy ndi LangChain:

DSPy

• dsp.split_dataset(data, target, test_size=0.2): Imagawanitsa gulu la data kukhala seti yophunzitsira ndi kuyesa.
• dsp.build_model(model_name, model_params): Amapanga makina ophunzirira pogwiritsa ntchito dzina lachitsanzo ndi magawo ena.
• dsp.train_model(model, data): Imaphunzitsa makina ophunzirira pa data yomwe yatchulidwa.
• dsp.evaluate_model(model, data): Imawunika momwe makina ophunzirira amagwirira ntchito pa data yomwe yatchulidwa.
• dsp.deploy_model(model, server_url): Imatumiza makina ophunzirira ku ulalo womwe watchulidwa.

LangChain

• lc::Agent::new(model): Amapanga wothandizira LangChain pogwiritsa ntchito chitsanzo chomwe chatchulidwa.
• lc::Dataset::new(zitsanzo): Amapanga deta ya LangChain pogwiritsa ntchito zitsanzo zomwe zatchulidwa.
• lc::train(wothandizira, dataset): Amaphunzitsa wothandizira LangChain pa dataset yomwe yatchulidwa.
• lc::evaluate(wothandizira, dataset): Imawunika momwe agent wa LangChain amagwirira ntchito pa data yomwe yatchulidwa.
• lc::deploy(agent, server_url): Amatumiza wothandizira wa LangChain ku ulalo wa seva womwe watchulidwa.

# Kuwona

Nayi chithunzithunzi chosavuta cha zomangamanga zoyendetsedwa ndi AI pogwiritsa ntchito Chart.js:

const ctx = document.getElementById('myChart').getContext('2d');
const chart = Tchati chatsopano (ctx, {
    mtundu: 'bar',
    zambiri: {
        zolemba: ['Nthawi Yophunzitsira', 'Inference Time', 'Model Accuracy'],
        zolemba: [{
            chizindikiro: 'DSPy',
            Zambiri: [10, 5, 90],
            backgroundColor: 'rgba(255, 99, 132, 0.2)',
            BorderColor: 'rgba(255, 99, 132, 1)',
            BorderWidth: 1
        }, {
            chizindikiro: 'LangChain',
            Zambiri: [8, 6, 92],
            backgroundColor: 'rgba(54, 162, 235, 0.2)',
            BorderColor: 'rgba(54, 162, 235, 1)',
            BorderWidth: 1
        }]
    },
    zosankha: {
        masikelo: {
            y: {
                startAtZero: zoona
            }
        }
    }
});

Chotchinga ichi chimapanga tchati chosavuta kugwiritsa ntchito Chart.js kuti muwone momwe DSPy ndi LangChain amagwirira ntchito.

##Mapeto

M'nkhaniyi, tafufuza njira za AI, uinjiniya wamapulogalamu, ndi DevOps, ndikugogomezera kwambiri kugwiritsa ntchito zida zoyimba ngati DSPy ndi LangChain kuphatikiza othandizira a AI mu mapaipi a DevOps. Tinawonetsa momwe tingagwiritsire ntchito zomangamanga zotetezeka komanso zowonongeka zoyendetsedwa ndi AI pogwiritsa ntchito DSPy ndi LangChain, ndipo tinapereka chitsanzo chophweka cha kupanga makina ophunzirira makina pogwiritsa ntchito chida chilichonse. Tinaperekanso 5-10 yapamwamba kwambiri ya malamulo / zidule zogwiritsidwa ntchito pa chida chilichonse ndi chithunzithunzi chosavuta cha zomangamanga zoyendetsedwa ndi AI pogwiritsa ntchito Chart.js.