I-ZeroOps: Ukuzuza Ingqalasizinda Ezimele Ngama-AI Agents, i-StackGen, ne-LangChain | B4N1

I-ZeroOps: Ukuzuza Ingqalasizinda Ezimele Ngama-AI Agents, i-StackGen, ne-LangChain

7 months ago · b4n1 Read time: 3 min

Ama-ZeroOps: Ukuzuza Ingqalasizinda Ezimele Ngama-AI Agents, i-StackGen, ne-LangChain

Isingeniso

Esimeni esiguqukayo njalo sezindawo ezihlala amafu, isidingo sokuphathwa kwengqalasizinda esebenzayo nenyukayo asikaze sibe phezulu. Amathuluzi endabuko e-automation abe nesandla ekuhleleni izinqubo, kodwa ngokuvamile awafinyeleli ekubhekaneni nobunzima kanye nama-nuances wengqalasizinda yesimanje. Yilapho i-ZeroOps ingena khona - indlela yoguquko esebenzisa i-Agentic AI ukuze kuzuzwe ukuphathwa kwengqalasizinda okuzimele. Kulesi sihloko, sizongena emhlabeni we-ZeroOps, sihlole izingxenye zawo eziyinhloko, izinzuzo, kanye nezinhlelo zokusebenza ezingokoqobo.

Yini i-ZeroOps?

I-ZeroOps ipharadigm evelayo ehlanganisa amandla e-Artificial Intelligence (AI) nokuguquguquka kwezindawo ezihlala amafu ukuze kudaleke ingqalasizinda ezilawulayo. Ngokusebenzisa amakhono ama-agent we-AI, i-StackGen, ne-LangChain, i-ZeroOps yenza izinhlangano zikwazi ukwenza ngokuzenzakalelayo ukuphathwa kwengqalasizinda, zehlise amaphutha abantu, futhi zithuthukise ukusebenza kahle okuphelele.

I-Agent AI: Inhliziyo Ye-ZeroOps

I-Agent AI ibhekisela ohlotsheni lwe-AI olubonisa ukuziphatha okuhlakaniphile, amakhono okwenza izinqumo, nokuzivumelanisa nezimo. Ngomongo we-ZeroOps, ama-ejenti e-Agentic AI aklanyelwe ukufunda kumaphethini wengqalasizinda, akhombe okudidayo, futhi athathe izinyathelo zokulungisa. Laba basebenzeli bangaqeqeshwa emithonjeni ehlukahlukene yedatha, okuhlanganisa amalogi, amamethrikhi, namafayela okulungiselela, ukuthuthukisa ukuqonda okujulile kokuziphatha kwengqalasizinda.

I-StackGen: Injini Yengqalasizinda-njenge-Code (IaC)

I-StackGen iyinjini ye-IaC yomthombo ovulekile eyenza izinhlangano zichaze futhi zilawule ingqalasizinda njengekhodi. Ngokusebenzisa i-StackGen, abathuthukisi bangakha izifanekiso zengqalasizinda, basebenzise izinhlelo zokusebenza, futhi baphathe izinsiza ngendlela engaguquki futhi ekwazi ukukhiqizwa kabusha. Ukuhlanganiswa kwe-StackGen nabasebenzeli be-Agentic AI kuvumela ukwakhiwa kwengqalasizinda yokuziphilisa, lapho ama-ejenti angathola futhi alungise izinkinga ngesikhathi sangempela.

LangChain: I-AI-Powered Infrastructure Orchestrator

I-LangChain iwuhlaka olusekelwe ku-Python oluvumela abathuthukisi ukuthi bakhe futhi basebenzise ama-orchestrator engqalasizinda anamandla e-AI. Ngokusebenzisa i-LangChain, izinhlangano zingakha ama-agent e-AI angokwezifiso ahlanganiswe ne-StackGen namanye amathuluzi engqalasizinda ukuze kuzuzwe ukuphathwa kwengqalasizinda okuzimele. Isakhiwo se-modular se-LangChain kanye nomtapo wolwazi obanzi wezingxenye ezakhelwe kusengaphambili zikwenza kube ukukhetha okuhle ekwakheni izixazululo eziyinkimbinkimbi zengqalasizinda esebenza nge-AI.

Ukusebenza Okusebenzayo: Isibonelo Esilula

Ake sicabangele isibonelo esilula sokuthi i-ZeroOps ingasetshenziswa kanjani endaweni yamafu. Ake sithi sinesicelo sewebhu esifakwe kuqoqo le-Kubernetes, futhi sifuna ukwenza ngokuzenzakalelayo inqubo yokukala uhlelo lokusebenza ngokusekelwe esidingweni sethrafikhi.


ngenisa i-langchain
kusuka ku-langchain.agents ingenisa i-LLMChain
kusuka ku-langchain.llms ngenisa i-LangChainLLM

# Chaza i-ejenti ye-AI
i-ejenti = LLMChain(
    llm=LangChainLLM(
        model_name="code-davinci-002",
        izinga lokushisa=0.7,
        max_tokens=2048,
        stop_sequences=["\n", "\n\n"]
    ),
    chain_name="scale_app"
)

# Chaza isifanekiso se-StackGen
isifanekiso = {
    "kind": "Ukuthunyelwa",
    "imethadatha": {
        "name": "web-app"
    },
    "spec": {
        "imifanekiso": 1
    }
}

# Chaza inqubomgomo yokukala
inqubomgomo = {
    "condition": "traffic > 1000",
    "isenzo": {
        "imifanekiso": 2
    }
}

# Dala i-orchestrator yengqalasizinda esebenza nge-AI
i-orchestrator = LangChainLLM(
    model_name="code-davinci-002",
    izinga lokushisa=0.7,
    max_tokens=2048,
    stop_sequences=["\n", "\n\n"]
)

# Chaza ukuthunyelwa kwe-StackGen
deployment = StackGen(
    isifanekiso=isifanekiso,
    inqubomgomo=inqubomgomo
)

# Faka isicelo
deployment.deploy()

# Kala uhlelo lokusebenza ngokususelwa kusidingo sethrafikhi
orchestrator.scale_app(i-ejenti, ukuthunyelwa)

Kulesi sibonelo, sichaze i-ejenti ye-AI esebenzisa i-LangChain ehlanganisa ne-StackGen ukuze ngokuzenzakalela inqubo yokukala uhlelo lokusebenza lwewebhu ngokusekelwe esidingweni sethrafikhi. Umenzeli usebenzisa imodeli yolimi eqeqeshwe kusengaphambili ukuze ahlaziye idatha yethrafikhi futhi enze izinqumo mayelana nokukala uhlelo lokusebenza.

Imiyalo/Amazwibela Aphezulu Okuyi-5-10

Nansi eminye yemiyalo evame ukusetshenziswa kakhulu namazwibela we-ZeroOps:

  1. langchain init: Iqalisa iphrojekthi entsha ye-LangChain.
langchain init my_project
  1. llm chain: Kwakha iketango elisha le-LLM.
kusuka ku-langchain.agents ingenisa i-LLMChain
kusuka ku-langchain.llms ngenisa i-LangChainLLM

i-ejenti = LLMChain(
    llm=LangChainLLM(
        model_name="code-davinci-002",
        izinga lokushisa=0.7,
        max_tokens=2048,
        stop_sequences=["\n", "\n\n"]
    ),
    chain_name="my_chain"
)
  1. i-stackgen deploy: Isebenzisa isifanekiso se-StackGen kumhlinzeki wamafu.
stackgen deploy my_template.yaml
  1. i-orchestrator scale_app: Ikala uhlelo lokusebenza ngokusekelwe esidingweni sethrafikhi.
kusuka ku-langchain.agents ingenisa i-LLMChain
kusuka ku-langchain.llms ngenisa i-LangChainLLM

i-orchestrator = LangChainLLM(
    model_name="code-davinci-002",
    izinga lokushisa=0.7,
    max_tokens=2048,
    stop_sequences=["\n", "\n\n"]
)

orchestrator.scale_app(i-ejenti, ukuthunyelwa)
  1. isitimela somenzeli: Iqeqesha i-ejenti ye-AI kudathasethi.
i-ejenti iqeqesha my_dataset.csv
  1. inqubomgomo ye-stackgen: Ichaza inqubomgomo yokukala yohlelo lokusebenza.
inqubomgomo = {
    "condition": "traffic > 1000",
    "isenzo": {
        "imifanekiso": 2
    }
}
  1. buka ngeso lengqondo: Ibona ngeso lengqondo inqubo yokuthatha izinqumo ye-ejenti ye-AI.
langchain ubone ngeso lengqondo my_agent.json
  1. isimo se-orchestrator: Ibonisa isimo sohlelo lokusebenza.
isimo se-orchestrator my_app
  1. amalogi e-stackgen: Ibonisa amalogi wokukhishwa kwe-StackGen.
stackgen izingodo my_deployment
  1. i-ejenti ihlola: Ihlola ukusebenza kwe-ejenti ye-AI.
i-ejenti ihlola i-my_agent.json

Ukubona

Nakhu okubonwayo kwezakhiwo ze-ZeroOps kusetshenziswa i-Chart.js:

{
  "type": "bar",
  "idatha": {
    "labels": ["Agent AI", "StackGen", "LangChain"],
    "idathasethi": [
      {
        "ilebula": "Izingxenye",
        "idatha": [10, 8, 12],
        "Umbala wangemuva": [
          "rgba(255, 99, 132, 0.2)",
          "rgba(54, 162, 235, 0.2)",
          "rgba(255, 206, 86, 0.2)"
        ],
        "Umbala Womngcele": [
          "rgba(255, 99, 132, 1)",
          "rgba(54, 162, 235, 1)",
          "rgba(255, 206, 86, 1)"
        ],
        "BorderWidth": 1
      }
    ]
  },
  "izinketho": {
    "izikali": {
      "yAxes": [
        {
          "imikhaza": {
            "beginAtZero": kuyiqiniso
          }
        }
      ]
    }
  }
}

Lokhu kubukwa kukhombisa izingxenye ezintathu ezibalulekile ze-ZeroOps: i-Agentic AI, i-StackGen, ne-LangChain. Ishadi lebha libonisa ukubaluleka okuhlobene kwengxenye ngayinye ekwakhiweni kweZeroOps.

Isiphetho

I-ZeroOps iyindlela yokuguquguquka ekuphathweni kwengqalasizinda esebenzisa i-Agentic AI, i-StackGen, ne-LangChain ukuze kuzuzwe ukuphathwa kwengqalasizinda okuzimele. Ngokusebenzisa amandla e-AI ne-IaC, i-ZeroOps yenza izinhlangano zikwazi ukwenza ngokuzenzakalelayo ukuphathwa kwengqalasizinda, zehlise amaphutha abantu, futhi zithuthukise ukusebenza kahle kukonke. Ngezinhlelo zayo ezisebenzayo, imiyalo ephezulu, nokubonwa ngamehlo, i-ZeroOps isilungele ukuguqula indlela esiphatha ngayo ingqalasizinda ezindaweni ezihlala amafu.

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