I-ZeroOps: Ukufezekiswa kweZiseko eziZimeleyo kunye nee-arhente ze-AI, iStackGen, kunye neLangChain | B4N1

I-ZeroOps: Ukufezekiswa kweZiseko eziZimeleyo kunye nee-arhente ze-AI, iStackGen, kunye neLangChain

7 months ago · b4n1 Read time: 4 min

I-ZeroOps: Ukufezekisa iZibonelelo eziZimeleyo kunye nee-Agent ze-AI, iStackGen, kunye neLangChain

Intshayelelo

Kumhlaba osoloko uvela wokusingqongileyo wendalo yamafu, imfuno yolawulo lweziseko ezingundoqo olusebenzayo nolunokwehla aluzange lube phezulu. Izixhobo ezizisebenzelayo zesiNtu ziye zaba luncedo kwiinkqubo zohlengahlengiso, kodwa zihlala zisilela ekujonganeni nobunzima kunye ne-nuances yeziseko zophuhliso lwangoku. Kulapho i-ZeroOps ingena khona-indlela yoguquko eyenza i-Agentic AI ifezekise ulawulo lweziseko ezingundoqo ezizimeleyo. Kweli nqaku, siza kungena kwihlabathi leZeroOps, sijonga izinto zalo eziphambili, izibonelelo, kunye nokusetyenziswa okusebenzayo.

Yintoni iZeroOps?

I-ZeroOps yiparadigm evelayo edibanisa amandla e-Artificial Intelligence (AI) kunye nokuguquguquka kweendawo zokuhlala zamafu ukuze zenze iziseko zokulawula ukuzilawula. Ngokusebenzisa amandla e-AI agents, i-StackGen, kunye ne-LangChain, i-ZeroOps yenza ukuba imibutho izenzele ulawulo lweziseko zophuhliso, ukunciphisa impazamo yabantu, kunye nokuphucula ukusebenza kakuhle.

Agentic AI: Intliziyo yeZeroOps

I-Agentic AI ibhekisa kuhlobo lwe-AI olubonisa ukuziphatha okukrelekrele, amandla okwenza izigqibo, kunye nokuguquguquka. Kumxholo weZeroOps, iiarhente zeAgentic AI ziyilelwe ukuba zifunde kwiipateni zeziseko zophuhliso, zichonge izinto ezingaqhelekanga, kwaye zithathe amanyathelo okulungisa. Ezi arhente zinokuqeqeshwa kwimithombo eyahlukeneyo yedatha, kubandakanywa iilogi, iimethrikhi, kunye neefayile zoqwalaselo, ukuphuhlisa ukuqonda okunzulu kokuziphatha kweziseko zophuhliso.

I-StackGen: I-injini ye-Infrastructure-as-Code (IaC)

I-StackGen yinjini ye-IaC evulelekileyo eyenza ukuba imibutho ichaze kwaye ilawule iziseko zophuhliso njengekhowudi. Ngokusebenzisa i-StackGen, abaphuhlisi banokwenza iitemplates zeziseko zophuhliso, basebenzise izicelo, kwaye balawule izixhobo ngendlela engaguqukiyo nenokuphinda iphindwe. Ukuhlanganiswa kwe-StackGen kunye ne-Agentic AI agents ivumela ukudala iziseko zokuziphilisa, apho ii-arhente ziyakwazi ukubona kunye nokulungisa imiba ngexesha langempela.

LangChain: I-AI-Powered Infrastructure Orchestrator

I-LangChain yinkqubo-sikhokelo esekwe kwiPython eyenza abaphuhlisi ukuba bakhe kwaye basebenzise ii-orchestrators zeziseko ezingundoqo ze-AI. Ngokusebenzisa i-LangChain, imibutho inokudala i-arhente ye-AI yesiqhelo edibanisa ne-StackGen kunye nezinye izixhobo zeziseko zophuhliso ukuze kufezekiswe ulawulo lweziseko ezizimeleyo. I-architecture yeemodyuli zeLangChain kunye nethala leencwadi elibanzi lamacandelo okwakhiwa kwangaphambili kwenza kube yinto efanelekileyo yokwakha izisombululo zeziseko ezingundoqo ze-AI.

Usetyenziso oluSebenzayo: Umzekelo olula

Makhe siqwalasele umzekelo olula wendlela iZeroOps enokusetyenziswa ngayo kwindawo yendalo yamafu. Masithi sinesicelo sewebhu esifakwe kwiqela le-Kubernetes, kwaye sifuna ukwenza ngokuzenzekelayo inkqubo yokulinganisa isicelo ngokusekelwe kwimfuno yetrafikhi.


ngenisa i-langchain
ukusuka langchain.agents ukungenisa LLMChain
ukusuka langchain.llms ukungenisa LangChainLLM

# Chaza iarhente ye-AI
iarhente = LLMChain(
    llm=LangChainLLM(
        imodeli_igama = "ikhowudi-davinci-002",
        iqondo lobushushu=0.7,
        max_tokens=2048,
        stop_sequences=["\n", "\n\n"]
    ),
    chain_name = "scale_app"
)

# Chaza ithemplate yeStackGen
ithempleyithi = {
    "kind": "Ukusasazwa",
    "metadata": {
        "igama": "i-web-app"
    },
    "umzekelo": {
        "iikopi": 1
    }
}

# Chaza umgaqo-nkqubo wokukala
umgaqo-nkqubo = {
    "condition": "traffic > 1000",
    "intshukumo": {
        "iikopi": 2
    }
}

# Yenza i-AI-powered okhestrator yeziseko ezingundoqo
okhestra = LangChainLLM(
    imodeli_igama = "ikhowudi-davinci-002",
    iqondo lobushushu=0.7,
    max_tokens=2048,
    stop_sequences=["\n", "\n\n"]
)

# Chaza ukuthunyelwa kwe-StackGen
ukusasazwa = StackGen(
    ithempleyithi=itemplate,
    umgaqo-nkqubo=umgaqo-nkqubo
)

# Faka isicelo
deployment.deploy()

# Ukulinganisa usetyenziso ngokusekwe kwimfuno yezithuthi
orchestrator.scale_app(iarhente, ukusasazwa)

Kulo mzekelo, sichaze i-arhente ye-AI esebenzisa i-LangChain edibanisa ne-StackGen ukwenza ngokuzenzekelayo inkqubo yokulinganisa isicelo sewebhu ngokusekelwe kwimfuno yetrafikhi. I-arhente isebenzisa imodeli yolwimi oluqeqeshwe kwangaphambili ukuhlalutya idatha yezithuthi kunye nokwenza izigqibo malunga nokulinganisa isicelo.

Ephezulu 5-10 Eyeyona Isetyenziswayo Imiyalelo/iziqwengana

Nantsi eminye yemiyalelo edla ngokusetyenziswa kunye neziqwengana zeZeroOps:

  1. langchain init: Iqalisa iprojekthi entsha yeLangChain.
langchain init my_project
  1. llm chain: Yenza ikhonkco elitsha leLLM.
ukusuka langchain.agents ukungenisa LLMChain
ukusuka langchain.llms ukungenisa LangChainLLM

iarhente = LLMChain(
    llm=LangChainLLM(
        imodeli_igama = "ikhowudi-davinci-002",
        iqondo lobushushu=0.7,
        max_tokens=2048,
        stop_sequences=["\n", "\n\n"]
    ),
    chain_name="my_chain"
)
  1. i-stackgen deploy: Isebenzisa itemplate ye-StackGen kumnikezeli welifu.
stackgen deploy my_template.yaml
  1. i-orchestrator scale_app: Ilinganisa usetyenziso ngokusekelwe kwimfuno yezithuthi.
ukusuka langchain.agents ukungenisa LLMChain
ukusuka langchain.llms ukungenisa LangChainLLM

okhestra = LangChainLLM(
    imodeli_igama = "ikhowudi-davinci-002",
    iqondo lobushushu=0.7,
    max_tokens=2048,
    stop_sequences=["\n", "\n\n"]
)

orchestrator.scale_app(iarhente, ukusasazwa)
  1. uloliwe we-arhente: Uqeqesha i-arhente ye-AI kwidathasethi.
ummeli uqeqeshe my_dataset.csv
  1. umgaqo-nkqubo we-stackgen: Uchaza umgaqo-nkqubo wokukala wesicelo.
umgaqo-nkqubo = {
    "condition": "traffic > 1000",
    "intshukumo": {
        "iikopi": 2
    }
}
  1. langchain visualize: Ibona ngeso lengqondo inkqubo yokwenza izigqibo ze-arhente ye-AI.
langchain yiba nombono my_agent.json
  1. imo ye-orchestrator: Ibonisa imo yesicelo.
Imo yeorchestrator yam_app
  1. i-stackgen logs: Ibonisa iilog zokusasazwa kwe-StackGen.
stackgen logs my_deployment
  1. i-arhente avavanya: Ivavanya ukusebenza kwe-arhente ye-AI.
ummeli uvavanye umy_agent.json

Umbono

Nantsi imbonakalo yoyilo lweZeroOps usebenzisa iChart.js:

{
  "type": "bar",
  "idatha": {
    "labels": ["Agent AI", "StackGen", "LangChain"],
    "iiseti zedatha": [
      {
        "ileyibhile": "Izixhobo",
        "idatha": [10, 8, 12],
        "umbala ongasemva": [
          "rgba(255, 99, 132, 0.2)",
          "rgba(54, 162, 235, 0.2)",
          "rgba(255, 206, 86, 0.2)"
        ],
        "Umbala womda": [
          "rgba(255, 99, 132, 1)",
          "rgba(54, 162, 235, 1)",
          "rgba(255, 206, 86, 1)"
        ],
        "BorderWidth": 1
      }
    ]
  },
  "iinketho": {
    "izikali": {
      "yAxes": [
        {
          "amanqaku": {
            "beginAtZero": yinyani
          }
        }
      ]
    }
  }
}

Oku kubonwa kubonisa amacandelo amathathu angundoqo e-ZeroOps: I-Agentic AI, i-StackGen, kunye ne-LangChain. Itshathi yebha ibonisa ukubaluleka kobudlelwane becandelo ngalinye kuyilo lweZeroOps.

Isiphelo

I-ZeroOps yindlela yokuguqula ulawulo lweziseko zophuhliso olusebenzisa i-Agentic AI, i-StackGen, kunye neLangChain ukufezekisa ulawulo lweziseko ezizimeleyo. Ngokusebenzisa amandla e-AI kunye ne-IaC, i-ZeroOps yenza ukuba imibutho izenzele ulawulo lweziseko ezingundoqo, ukunciphisa impazamo yabantu, kunye nokuphucula ukusebenza kakuhle. Ngokusetyenziswa kwayo okusebenzayo, imiyalelo ephezulu, kunye nokubonwayo, iZeroOps ikulungele ukuguqula indlela esilawula ngayo iziseko zophuhliso kwiindawo ezihlala ilifu.

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