Ịmepụta Scalable na Ethical AI-Driven Software Systems na Modular Workflow Architecture na AI Agents. | B4N1

Ịmepụta Scalable na Ethical AI-Driven Software Systems na Modular Workflow Architecture na AI Agents.

5 months ago · b4n1 Read time: 6 min

Okwu mmalite

Ọganihu ngwa ngwa na Artificial Intelligence (AI) na injinịa sọftụwia ebutela mmepe nke sistemu sọftụwia dị mgbagwoju anya na-ejikọ ndị nnọchi anya AI na ụkpụrụ ụkpụrụ arụ ọrụ modular. Sistemu ndị a nwere ike ịgbanwe ụzọ anyị si aga n'ihu DevOps automation na orchestration. Agbanyeghị, imepụta sistemu sọftụwia AI na-anya nke ọma chọrọ nghọta miri emi maka ọganihu ọhụrụ na AI, injinịa ngwanrọ na ụkpụrụ ụlọ.

N'isiokwu a, anyị ga-enyocha ụkpụrụ imewe na omume kachasị mma maka ịmepụta modular, scalable, na usoro ngwanrọ na-ejikọta ndị ọrụ AI na nhazi usoro ọrụ modul. Anyị ga-elekwasị anya na ijikọta ọganihu kachasị ọhụrụ na AI (Large Language Models (LLMs), Retrieval-Augmented Generation (RAG)), injinịa ngwanrọ (Python, Rust, Go), na ihe owuwu iji mepụta usoro siri ike ma na-edozi.

Modular Workflow Architecture

Modul arụ ọrụ na-arụ ọrụ bụ ụkpụrụ nke na-akụda sistemu gbagwojuru anya n'ime obere, modul nweere onwe ya nke enwere ike idokwa ya ngwa ngwa, emelite na nha. Modul ọ bụla na-ahụ maka otu ọrụ ma ọ bụ ọrụ dị iche iche, ha na-ekwurịta okwu n'etiti ibe ha site na oghere ndị akọwapụtara nke ọma.

Nke a bụ ihe atụ dị mfe nke ụkpụrụ ụlọ ọrụ na-arụ ọrụ na Python: "Python

workflow.py

Usoro ọrụ klas: def init(onwe): self.modules = []

def add_module (onwe, modul):
    self.modules.append(modul)

def ọsọ (onwe):
    maka modul na self.modules:
        modul.run()

modul1.py

Modul klas 1: def ọsọ (onwe): ebipụta ("Modul 1 na-agba ọsọ")

modul2.py

Modul 2 klas: def ọsọ (onwe): ebipụta ("Modul 2 na-agba ọsọ")

isi.py

ọ bụrụ aha == "__ isi__": usoro ọrụ = usoro ọrụ () workflow.add_module(Module1()) workflow.add_module(Module2()) workflow.run() `` N'ihe atụ a, anyị nwere klaasị 'Workflow' na-ejikwa ndepụta modul. Modul ọ bụla na-ahụ maka otu ọrụ, a na-agbakwunye ya na usoro ọrụ site na iji usoro 'add_module'. A na-eji usoro 'run' iji mebie modul ọ bụla na usoro ọrụ.

Ndị nnọchi anya AI

Ndị nnọchi anya AI bụ ngwa ngwanrọ na-eji algọridim mmụta igwe mee mkpebi na ime ihe na sistemụ. Enwere ike iji ha rụọ ọrụ arụ ọrụ, melite ahụmịhe onye ọrụ, yana kwalite arụmọrụ sistemụ.

Enwere ọtụtụ ụdị ndị ọrụ AI, gụnyere:

** Ndị ọrụ dabere na iwu ***: Ndị ọrụ ndị a na-eji iwu akọpụtagoro eme mkpebi. ** Ndị ọrụ mmụta igwe ***: Ndị ọrụ a na-eji algọridim mmụta igwe iji mụta na data wee mee mkpebi. ** Ndị nnọchi anya ngwakọ ***: Ndị nnọchi anya ndị a na-ejikọta usoro mmụta dabere na iwu na igwe iji mee mkpebi.

Nke a bụ ọmụmaatụ nke onye nnọchi anya AI dị mfe na Python na-eji igwe mmụta algọridim: "Python

onye nnọchi anya.py

si sklearn.ensemble mbubata RandomForestClassifier si sklearn.model_selection mbubata train_test_split si sklearn.datasets bubata load_iris

Onye nnọchi anya klas: def init(onwe): self.model = RandomForestClassifier()

def ụgbọ oloko (onwe, X, y):
    X_train, X_test, y_train, y_test = train_test_split (X, y, test_size=0.2, random_state=42)
    self.model.fit (X_train, y_train)

def amụma (onwe, X):
    weghachi self.model.predict(X)

isi.py

ọ bụrụ aha == "__ isi__": iris = load_iris () X = iris.data y = iris. target onye nnọchi anya = onye nnọchi anya () agent.train (X, y) amụma = agent.predict(X) ebipụta( amụma) `` N'ihe atụ a, anyị nwere onye ọrụ AI dị mfe nke na-eji nhazi oke ọhịa na-enweghị ihe ọ bụla iji mee amụma. A zụrụ onye nnọchi anya na iris dataset ma na-ebu amụma n'otu dataset ahụ.

Na-ejikọta ndị nnọchi anya AI na Modular Workflow Architecture

Ijikọta ndị ọrụ AI na ụkpụrụ ụkpụrụ arụ ọrụ modular na-enye anyị ohere ịmepụta sistemu dị mgbagwoju anya na-ejikọta mkpebi AI na-achịkwa ya na ụkpụrụ ụlọ nwere ike ịgbatị.

Nke a bụ ọmụmaatụ nke sistemụ na-ejikọta ndị nnọchi anya AI na ụkpụrụ arụ ọrụ modular: "Python

workflow.py

Usoro ọrụ klas: def init(onwe): self.modules = [] self.agent = Ọ dịghị

def add_module (onwe, modul):
    self.modules.append(modul)

def add_agent (onwe, onye nnọchi anya):
    self.agent = onye nnọchi anya

def ọsọ (onwe):
    maka modul na self.modules:
        modul.run()
    ọ bụrụ self.agent:
        self.agent. amụma()

modul1.py

Modul klas 1: def ọsọ (onwe): ebipụta ("Modul 1 na-agba ọsọ")

modul2.py

Modul 2 klas: def ọsọ (onwe): ebipụta ("Modul 2 na-agba ọsọ")

onye nnọchi anya.py

si sklearn.ensemble mbubata RandomForestClassifier si sklearn.model_selection mbubata train_test_split si sklearn.datasets bubata load_iris

Onye nnọchi anya klas: def init(onwe): self.model = RandomForestClassifier()

def ụgbọ oloko (onwe, X, y):
    X_train, X_test, y_train, y_test = train_test_split (X, y, test_size=0.2, random_state=42)
    self.model.fit (X_train, y_train)

def amụma (onwe, X):
    weghachi self.model.predict(X)

isi.py

ọ bụrụ aha == "__ isi__": usoro ọrụ = usoro ọrụ () workflow.add_module(Module1()) workflow.add_module(Module2()) onye nnọchi anya = onye nnọchi anya () agent.train (X, y) workflow.add_agent (onye nnọchi anya) workflow.run() `` N'ihe atụ a, anyị nwere sistemụ na-ejikọta ndị nnọchi anya AI na ụkpụrụ nhazi usoro arụ ọrụ. Usoro ahụ nwere modul abụọ na onye nnọchi anya AI nke na-eji nhazi oke ọhịa na-enweghị usoro iji mee amụma. A na-agbakwunye onye ọrụ ahụ na usoro ọrụ ahụ site na iji usoro 'add_agent`, ọ na-ebu amụma mgbe modul gbachara.

Top 5-10 Iwu/Snippets kacha eji

Nke a bụ ụfọdụ n'ime iwu na snippets ndị a na-ejikarị maka imepụta sistemu ngwanrọ AI na-akwado ụkpụrụ nwere ụkpụrụ arụ ọrụ na-arụ ọrụ na ndị nnọchi anya AI:

  1. Python: bubata pandas ka pd (na-ebubata pandas ọba akwụkwọ)
  2. Python: pd.read_csv('data.csv') (na-agụ faịlụ CSV n'ime pandas dataframe)
  3. ** Python ***: agent.train (X, y) (ịzụ onye nnọchi anya AI na dataset)
  4. ** Python **: agent.predict (X) (na-eme amụma site na iji onye nnọchi anya AI)
  5. ** nchara **: let mut agent = Agent :: ọhụrụ () (ịmepụta onye ọrụ AI ọhụrụ na Rust)
  6. ** nchara ***: agent.train (X, y) (ịzụ onye ọrụ AI na dataset na nchara)
  7. ** Gaa **: agent: = NewAgent () (ịmepụta onye nnọchi anya AI ọhụrụ na Go)
  8. **Gaa ***: agent.Train (X, y) (ịzụ onye ọrụ AI na dataset na Go)
  9. Chart.js: `const chart = ọhụrụ Chart (ctx, {ụdị: 'bar', data: {labels: ['Label 1', 'Label 2'], datasets: [{label: 'Dataset 1', data: [10, 20]}]]}}});" (ịmepụta chaatị.j) iji jiri chaatị.
  10. ** Chart.js ***: chart.data.datasets[0].data.push(30) (na-agbakwunye ihe ọhụrụ data na eserese iji Chart.js)

Nlere anya

Nke a bụ ọmụmaatụ chaatị mmanya na-eji Chart.js: Json { "ụdị": "ogwe", "data": { "Labels": ["Akara 1", "Akara 2"], "datasets": [ { "label": "Dataset 1", "data": [10, 20] } ] } } `` Chaatị a nwere akara abụọ yana otu dataset nwere isi data abụọ. Enwere ike ịhazi eserese ahụ site na iji nhọrọ na ntọala dị iche iche.

Mmechi

Ịmepụta sistemu sọftụwia AI na-akpachapụ anya na ụkpụrụ nwere ụkpụrụ arụ ọrụ na-arụ ọrụ na ndị nnọchi anya AI chọrọ nghọta miri emi nke ọganihu ọhụrụ na AI, injinịa ngwanrọ, na ụkpụrụ ụlọ. Site na ijikọ ndị nnọchi anya AI na ụkpụrụ ụkpụrụ arụ ọrụ modular, anyị nwere ike ịmepụta sistemu dị mgbagwoju anya na-ejikọta mkpebi AI na-achịkwa ya na ụkpụrụ ụlọ nwere ike ịgbatị.

N'edemede a, anyị enyochala ụkpụrụ imewe na omume kachasị mma maka ịmepụta modular, scalable, na usoro sọftụwia ziri ezi nke na-ejikọta ndị ọrụ AI na usoro nhazi ọrụ modular. Anyị ewepụtakwala ihe atụ nke ndị ọrụ AI dị mfe na ụkpụrụ arụ ọrụ na-arụ ọrụ na Python, Rust, na Go, yana ihe atụ nhụta site na iji Chart.js.

Site n'ịgbaso ntuziaka na omume kachasị mma edepụtara n'isiokwu a, ndị mmepe nwere ike ịmepụta usoro ngwanrọ AI na-akwado siri ike na nke na-egbo mkpa ngwa na ụlọ ọrụ ọgbara ọhụrụ.

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