ZeroOps: Kuwana Autonomous Infrastructure neAI Agents, StackGen, uye LangChain

ZeroOps: Kuwana Autonomous Infrastructure neAI Agents, StackGen, uye LangChain

Introduction

Munzvimbo inogara ichishanduka-shanduka yemakore-ekuzvarwa nharaunda, kudiwa kwekuita kwakanaka uye kunotyisa maneja manejimendi hakuna kumbokwira. Maturusi echinyakare otomatiki akabatsira mukugadzirisa maitiro, asi anowanzo kutadza kugadzirisa kuomarara uye nuances yezvivakwa zvemazuva ano. Apa ndipo panouya ZeroOps - nzira yeshanduko iyo inokwirisa Agentic AI kuti iwane yakazvimirira manejimendi manejimendi. Muchinyorwa chino, isu tichapinda munyika yeZeroOps, tichiongorora ayo epakati zvikamu, mabhenefiti, uye anoshanda maapplication.

Chii chinonzi ZeroOps?

ZeroOps paradigm iri kusimukira inosanganisa simba reArtificial Intelligence (AI) nekushanduka-shanduka kwenzvimbo dzemakore-yekuzvarwa kuti vagadzire zvigadziriso zvekuzvitonga. Nekushandisa kugona kweAI vamiririri, StackGen, uye LangChain, ZeroOps inogonesa masangano kuti aite otomatiki manejimendi ezvivakwa, kuderedza kukanganisa kwevanhu, uye kunatsiridza kugona kwese.

**Agent AI: Mwoyo weZeroOps **

Agentic AI inoreva rudzi rweAI inoratidza hungwaru maitiro, kugona kuita sarudzo, uye kuchinjika. Muchirevo cheZeroOps, maAgentic AI vamiririri vakagadzirirwa kudzidza kubva pamatanho ezvivakwa, kuona zvinokanganisa, uye kutora matanho ekugadzirisa. Vamiririri ava vanogona kudzidziswa pane akasiyana data masosi, kusanganisira matanda, metrics, uye mafaera ekugadzirisa, kukudziridza kunzwisisa kwakadzama kwemaitiro ezvivakwa.

StackGen: Iyo Infrastructure-se-Code (IaC) Injini

StackGen ndeye yakavhurika-sosi IaC injini inogonesa masangano kutsanangura uye kubata zvivakwa sekodhi. Nekushandisa StackGen, vanogadzira vanogona kugadzira matemplate ezvivakwa, kutumira maapplication, uye kubata zviwanikwa nenzira inowirirana uye inogoneka. Kubatanidzwa kweStackGen neAgentic AI vamiririri inobvumira kugadzirwa kwekuzviporesa zvivakwa, uko vamiririri vanogona kuona nekugadzirisa nyaya munguva chaiyo.

**LangChain: Iyo AI-Powered Infrastructure Orchestrator **

LangChain iPython-based framework inogonesa vanogadzira kuvaka uye kuendesa AI-powered zvivakwa orchestrators. Nekusimudzira LangChain, masangano anogona kugadzira tsika dzeAI vamiririri vanobatana neStackGen uye mamwe maturusi ezvivakwa kuti vakwanise kuzvitonga zvivakwa. LangChain's modular architecture uye yakakura raibhurari yezvinhu zvakafanovakwa zvinoita sarudzo yakanaka yekuvaka yakaoma AI-powered zvigadziriso zvigadziriso.

**Kushanda Kushandisa: Muenzaniso Wakareruka **

Ngatitarisei muenzaniso wakapfava wekuti ZeroOps inogona kushandiswa sei munzvimbo yegore-yekuzvarwa. Ngatitii isu tine webhu application yakaiswa pane Kubernetes cluster, uye isu tinoda kugadzirisa otomatiki maitiro ekuyera application zvichienderana nekudiwa kwetraffic.


import langchain
kubva langchain.agents import LLMChain
kubva langchain.llms import LangChainLLM

# Tsanangura iyo AI mumiriri
mumiririri = LLMChain(
    llm=LangChainLLM(
        model_name="code-davinci-002",
        tembiricha=0.7,
        max_tokens=2048,
        stop_sequences=["\n", "\n\n"]
    ),
    chain_name = "scale_app"
)

# Tsanangura iyo StackGen template
template = {
    "kind": "Deployment",
    "metadata": {
        "zita": "web-app"
    },
    "spec": {
        "Replicas": 1
    }
}

# Tsanangura iyo yekuyera mutemo
mutemo = {
    "condition": "traffic > 1000",
    "chiito": {
        "Replicas": 2
    }
}

# Gadzira iyo AI-powered zvivakwa orchestrator
orchestrator = LangChainLLM(
    model_name="code-davinci-002",
    tembiricha=0.7,
    max_tokens=2048,
    stop_sequences=["\n", "\n\n"]
)

# Tsanangura iyo StackGen kuendesa
deployment = StackGen(
    template=template,
    mutemo=mutemo
)

# Shandisa iyo application
deployment.deploy()

# Skeya iyo application zvichienderana nekudiwa kwetraffic
orchestrator.scale_app(agent, deployment)

Mumuenzaniso uyu, takatsanangura mumiriri weAI anoshandisa LangChain inobatana neStackGen kuti iite otomatiki maitiro ekuyera webhu application zvichienderana nekudiwa kwetraffic. Mumiririri anoshandisa pre-yakadzidziswa mutauro modhi kuongorora traffic traffic uye kuita sarudzo nezve kuyera application.

Pamusoro 5-10 Inonyanya Kushandiswa Mirairo/Zvidimbu

Heano mamwe einonyanya kushandiswa mirairo uye snippets yeZeroOps:

langchain init: Inotanga chirongwa chitsva cheLangChain.

langchain init my_project

llm cheni: Inogadzira cheni itsva yeLLM.

kubva langchain.agents import LLMChain
kubva langchain.llms import LangChainLLM

mumiririri = LLMChain(
    llm=LangChainLLM(
        model_name="code-davinci-002",
        tembiricha=0.7,
        max_tokens=2048,
        stop_sequences=["\n", "\n\n"]
    ),
    chain_name="my_chain"
)

stackgen deploy: Inotumira StackGen template kune cloud provider.

stackgen deploy my_template.yaml

** orchestrator scale_app **: Inoyera application zvichienderana nekudiwa kwetraffic.

kubva langchain.agents import LLMChain
kubva langchain.llms import LangChainLLM

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

orchestrator.scale_app(agent, deployment)

chitima chemumiririri: Inodzidzisa mumiririri weAI pane dataset.

mumiririri chitima my_dataset.csv

stackgen policy: Inotsanangura mutemo wekuyera wekushandisa.

mutemo = {
    "condition": "traffic > 1000",
    "chiito": {
        "Replicas": 2
    }
}

langchain visualize: Inoratidza mumiririri weAI maitiro ekuita sarudzo.

langchain ona my_agent.json

** orchestrator mamiriro **: Inoratidza mamiriro ekushandisa.

orchestrator mamiriro my_app

** stackgen logs **: Inoratidza matanda eStackGen deployment.

stackgen matanda my_deployment

** mumiririri anoongorora **: Inoongorora kushanda kweAI mumiririri.

mumiririri anoongorora my_agent.json

Kuona

Heano tarisiro yeZeroOps architecture uchishandisa Chart.js:

{
  "type": "bha",
  "data": {
    "labels": ["Agent AI", "StackGen", "LangChain"],
    "datasets": [
      {
        "label": "Zvikamu",
        "data": [10, 8, 12],
        "backgroundColor": [
          "rgba(255, 99, 132, 0.2)",
          "rgba(54, 162, 235, 0.2)",
          "rgba(255, 206, 86, 0.2)"
        ],
        "borderColor": [
          "rgba(255, 99, 132, 1)",
          "rgba(54, 162, 235, 1)",
          "rgba(255, 206, 86, 1)"
        ],
        "borderWidth": 1
      }
    ]
  },
  "sarudzo": {
    "zvikero": {
      "yAxes": [
        {
          "zvikwekwe": {
            "beginAtZero": chokwadi
          }
        }
      ]
    }
  }
}

Ichi chitarisiko chinoratidza izvo zvitatu zvakakosha zvikamu zveZeroOps: Agentic AI, StackGen, uye LangChain. Iyo bar chart inoratidza kukosha kwakaenzana kwechikamu chimwe nechimwe muZeroOps architecture.

Mhedziso

ZeroOps inzira yekuchinja kune manejimendi ezvivakwa iyo inosimudzira Agentic AI, StackGen, uye LangChain kuti iwane yakazvimirira manejimendi manejimendi. Nekushandisa simba reAI neIaC, ZeroOps inogonesa masangano kuti aite otomatiki manejimendi ezvivakwa, kuderedza kukanganisa kwevanhu, uye kunatsiridza kushanda zvakanaka. Nemashandisirwo ayo anoshanda, mirairo yepamusoro, uye kuona, ZeroOps yakagadzirira kushandura nzira yatinobata nayo zvivakwa munzvimbo dzemakore-yekuzvarwa.

ZeroOps: Kuwana Autonomous Infrastructure neAI Agents, StackGen, uye LangChain

¿Tienes preguntas?