Folasaga
O le tuʻufaʻatasia o le Artificial Intelligence (AI) i polokalama faakomepiuta ua suia ai le auala tatou te faʻalatalata ai faʻafitauli-faʻafitauli, faia o filifiliga, ma masini. Ae peita'i, a'o fa'atupula'ia fa'alavelave fa'aoga a AI, o le mana'oga mo le fa'amautu ma le fa'aleleia o fausaga ua avea ma popolega tele. I totonu o lenei tusiga, o le a matou suʻesuʻeina le fesoʻotaʻiga o AI, masini komepiuta, ma DevOps, faʻatasi ai ma se faʻamamafa faʻapitoa i le faʻaogaina o meafaigaluega faʻapipiʻi e pei o DSPy ma LangChain e tuʻufaʻatasia AI sui i totonu o pipelines DevOps.
O Lu'itau o Fa'ata'ita'iga Polokalama Fa'atonu a AI
O talosaga fa'akomepiuta fa'ale AI e tu'uina atu ni lu'itau tulaga ese i inisinia fa'akomepiuta ma 'au a DevOps. O nisi o luitau autu e aofia ai:
- Scalability: O fa'ata'ita'iga AI e mana'omia ni puna'oa fa'akomepiuta taua, lea e mafai ona o'o atu ai i fa'afitauli fa'alautele i siosiomaga gaosiga.
- Saogalemu: O fa'ata'ita'iga AI e mafai ona a'afia i osofa'iga, e pei o fa'amatalaga oona ma fa'ata'ita'iga fa'afoliga osofa'iga.
- Tu'ufa'atasia: O fa'ata'ita'iga AI e mana'omia ona tu'ufa'atasia ma faiga fa'akomepiuta o lo'o iai, e mafai ona avea ma se galuega faigata.
- Mata'aiga ma Tausiga: O fa'ata'ita'iga AI e mana'omia le mata'ituina pea ma le tausiga ina ia mautinoa e sa'o ma fa'atuatuaina.
Meafaigaluega: DSPy ma LangChain
O mea faigaluega faʻapipiʻi e pei o DSPy ma LangChain e maua ai se fofo i nei luʻitau e ala i le faʻatagaina o le tuʻufaʻatasia o sui AI i totonu o pipelines DevOps. O nei meafaigaluega e tuʻuina atu ai se faʻavae mo le fausiaina, faʻapipiʻiina, ma le puleaina o faʻataʻitaʻiga AI, faʻamautinoaina le malupuipuia ma le faʻaogaina o le fausaga faʻapitoa mo faʻaoga faʻatautaia AI.
DSPy
O le DSPy o se meafaigaluega faʻapipiʻi punaʻoa e fausia i luga o le Python. E maua ai se API faigofie ma faʻaogaina mo le fausiaina, faʻapipiʻiina, ma le puleaina o faʻataʻitaʻiga AI. E lagolagoina e DSPy le tele o fa'avae AI, e aofia ai le TensorFlow, PyTorch, ma le scikit-learn.
LangChain
LangChain o se isi meafaigaluega faʻapipiʻi punaʻoa e maua ai se faʻavae mo le fausiaina, faʻapipiʻiina, ma le puleaina o faʻataʻitaʻiga AI. LangChain o loʻo fausia i luga o le Rust ma tuʻuina atu se API maualuga mo le tuʻufaʻatasia o sui AI i totonu o laina paipa DevOps.
Fa'atinoina o le Fa'ata'ita'iga Fa'atonu a le AI-Driven Software ma le DSPy
I totonu o lenei vaega, o le a matou suʻesuʻeina se faʻataʻitaʻiga faigofie o le faʻaogaina o le faʻaogaina o masini komepiuta faʻapipiʻi saogalemu ma faʻalauteleina AI ma le DSPy.
Fa'ata'ita'iga: Fausiaina o se Fa'ata'ita'iga Faigofie AI ma le DSPy
Sei o tatou mafaufau i se faʻataʻitaʻiga faigofie o le fausiaina o se faʻataʻitaʻiga aʻoaʻoga masini e faʻaaoga ai le DSPy. O le a matou fa'aogaina le faletusi scikit-learn e fausia ai se fa'ata'ita'iga fa'asologa fa'asolosolo.
faaulufale mai le dspy pei o le dsp
mai le sklearn.linear_model import LinearRegression
mai le sklearn.datasets faaulufale mai load_diabetes
# Tu'u le fa'amaumauga o le ma'i suka
fa'amaumauga = load_diabetes()
# Vaevae faʻamaumauga i aʻoaʻoga ma seti suʻega
X_train, X_test, y_train, y_test = dsp.split_dataset(dataset.data, dataset.target, test_size=0.2)
# Fausia se faʻataʻitaʻiga faʻasolosolo laina
fa'ata'ita'iga = LinearRegression()
# Aoao le faʻataʻitaʻiga i luga o faʻamaumauga aʻoaʻoga
model.fit(X_train, y_train)
# Iloilo le faʻataʻitaʻiga i faʻamaumauga o suʻega
sa'o = model.score(X_test, y_test)
lolomi(f"Fa'ata'ita'iga sa'o: {sa'o:.2f}")
O lenei poloka code o loʻo faʻaalia ai pe faʻapefea ona fausia se faʻataʻitaʻiga faʻataʻitaʻiga faʻaoga masini e faʻaaoga ai le DSPy. Matou te utaina faʻamaumauga o le maʻisuka, vaevae i seti aʻoaʻoga ma suʻega, fausia se faʻataʻitaʻiga faʻasolosolo laina, toleni i luga o faʻamaumauga o aʻoaʻoga, ma iloilo lona saʻo i faʻamaumauga o suʻega.
Fa'atinoina le Saogalemu ma Fa'asalaina AI-Driven Software Architecture ma LangChain
I totonu o lenei vaega, o le a matou suʻesuʻeina se faʻataʻitaʻiga faigofie o le faʻatinoina o le faʻaogaina o masini komepiuta faʻapipiʻi saogalemu ma faʻalauteleina AI ma LangChain.
Faʻataʻitaʻiga: Fausiaina o se Faʻataʻitaʻiga Faʻafaigofie AI ma LangChain
Sei o tatou mafaufau i se faʻataʻitaʻiga faigofie o le fausiaina o se faʻataʻitaʻiga aʻoaʻoga masini e faʻaaoga ai LangChain. Matou te faʻaogaina le faletusi TensorFlow e fausia ai se faʻataʻitaʻiga fesoʻotaʻiga neural.
fa'aaoga le langchain e pei o le lc;
fa'aoga tensorflow::prelude::*;
// Fa'amatala se fa'ata'ita'iga faigofie feso'ota'iga neural
fn fa'ata'ita'iga(fa'aoga: &Tensor) -> Tensor {
tu'u galuega = input.matmul(&Tensor::new(1, 1));
galuega faatino
}
// Fausia se sui LangChain
tu'u sui = lc::Agent::new(model);
// Fa'amatala se fa'amaumauga
tu'u dataset = lc::Dataset::new(vec![
lc::Fa'aa'oa'oga::fou(vec![1.0, 2.0], vec![3.0]),
lc::Fa'aa'oa'oga::fou(vec![4.0, 5.0], vec![6.0]),
]);
// A'oa'o le fa'ata'ita'iga ile fa'amaumauga
agent.train(&faamaumauga);
// Iloilo le faʻataʻitaʻiga i luga o faʻamaumauga
tu'u sa'o = agent.evaluate(&dataset);
println!("Fa'ata'ita'iga sa'o: {}", sa'o);
O lenei poloka code o loʻo faʻaalia ai le auala e fausia ai se faʻataʻitaʻiga faʻataʻitaʻiga faʻaoga masini e faʻaaoga ai LangChain. Matou te faʻamalamalamaina se faʻataʻitaʻiga faigofie o fesoʻotaʻiga neural, fatuina se sui LangChain, faʻamalamalamaina se faʻamaumauga, aʻoaʻoina le faʻataʻitaʻiga i luga o faʻamaumauga, ma iloilo lona saʻo i luga o faʻamaumauga.
Top 5-10 Poloaiga/Snippets Tele Fa'aaogaina
O le pito i luga 5-10 pito sili ona faʻaaogaina poloaiga / snippets mo DSPy ma LangChain:
DSPy
dsp.split_dataset(data, target, test_size=0.2): Vaevae fa'amaumauga i seti a'oa'oga ma su'ega.dsp.build_model(model_name, model_params): Fausia se faʻataʻitaʻiga aʻoaʻoga masini e faʻaaoga ai le igoa faʻataʻitaʻi ma faʻamaufaʻailoga.dsp.train_model(model, data): A'oa'o le fa'ata'ita'iga a'oa'oga masini i fa'amaumauga fa'apitoa.dsp.evaluate_model(model, data): Iloilo le faatinoga o le masini a'oa'oga fa'ata'ita'iga i fa'amaumauga fa'apitoa.dsp.deploy_model(model, server_url): Fa'atino le fa'ata'ita'iga a'oa'oga masini i le URL fa'atonu.
LangChain
lc::Agent::new(model): Fausia se sui LangChain e faʻaaoga ai le faʻataʻitaʻiga faʻapitoa.lc::Dataset::new(fa'ata'ita'iga): Fausia se fa'amaumauga LangChain e fa'aaoga ai fa'ata'ita'iga fa'apitoa.lc::train(agent, dataset): Toleni le sui LangChain i luga o faʻamaumauga faʻamaonia.lc :: iloilo (suesue, faʻamaumauga): Iloilo le faʻatinoga o le LangChain sui i luga o faʻamaumauga faʻamaonia.lc::deploy(agent, server_url): Deploy le LangChain agent i le URL server patino.
Va'aiga
Ole fa'aaliga faigofie lea ole fa'ata'ita'iga fa'akomepiuta e fa'aogaina e AI e fa'aaoga ai le Chart.js:
const ctx = document.getElementById('myChart').getContext('2d');
siata fa'amau = siata fou(ctx, {
ituaiga: 'pa',
fa'amaumauga: {
fa'ailoga: ['Taimi a'oa'oga', 'Taimi Fa'ailoga', 'Sa'o Fa'atusa'],
fa'amaumauga: [{
fa'ailoga: 'DSPy',
fa'amaumauga: [10, 5, 90],
lanu: 'rgba(255, 99, 132, 0.2)',
Lanu tuaoi: 'rgba(255, 99, 132, 1)',
tuaoiLātele: 1
}, {
igoa: 'LangChain',
fa'amaumauga: [8, 6, 92],
lanu: 'rgba(54, 162, 235, 0.2)',
Lanu tuaoi: 'rgba(54, 162, 235, 1)',
tuaoiLātele: 1
}]
},
filifiliga: {
fua: {
y: {
beginAtZero: moni
}
}
}
});
O lenei poloka code e fatuina ai se siata paʻu faigofie e faʻaaoga ai le Chart.js e faʻaalia ai le faʻatinoga o DSPy ma LangChain.
Fa'ai'uga
I totonu o lenei tusiga, na matou suʻesuʻeina le fesoʻotaʻiga o AI, inisinia software, ma DevOps, faʻatasi ai ma se faʻamamafa faʻapitoa i le faʻaogaina o meafaigaluega faʻapipiʻi e pei o DSPy ma LangChain e tuʻufaʻatasia AI sui i totonu o pipelines DevOps. Na matou faʻaalia le faʻaogaina o le faʻaogaina o le faʻaogaina o le AI-driven software architecture e faʻaaoga ai le DSPy ma le LangChain, ma tuʻuina atu se faʻataʻitaʻiga faigofie o le fausiaina o se faʻataʻitaʻiga aʻoaʻoga masini e faʻaaoga ai meafaigaluega taʻitasi. Na matou tuʻuina atu foʻi se pito i luga 5-10 pito sili ona faʻaaogaina poloaiga / snippets mo meafaigaluega taʻitasi ma se faʻaaliga faigofie o le faʻaogaina o polokalama faakomepiuta AI e faʻaaoga ai le Chart.js.