StackGen ifayile yoqwalaselo (example.yaml) | B4N1

StackGen ifayile yoqwalaselo (example.yaml)

8 months ago · b4n1 Read time: 3 min

I-Ochestration yeZiseko eziZimeleyo kunye ne-StackGen kunye neLangChain: Ukusetyenziswa kwee-Agent ze-AI kunye neeModeli zokuVelisa kwi-Autonomous DevOps Automation kunye noLawulo lweZiseko eziZimeleyo

Intshayelelo

Kwimeko yedijithali yanamhlanje ekhawulezayo, imfuno yolawulo lweziseko ezingundoqo olusebenzayo nolunokwehla aluzange lube phezulu. Izenzo ze-DevOps zeNdabuko zihlala zixhomekeke kwi-manual configuration kunye nescripting, ezinokukhokelela kwiimpazamo, ukungahambelani, kunye neebhotile. Ukujongana nale mingeni, sibona ukuvela kwe-orchestration yeziseko zophuhliso ezizimeleyo, ipharadigm exhasa iiarhente ze-AI kunye neemodeli eziveliswayo ukuze zizenzekele kwaye ziphucule ulawulo lweziseko ezingundoqo. Kweli nqaku, siza kungena kwihlabathi elinomdla le-StackGen kunye ne-LangChain, iitekhnoloji ezimbini eziphambili eziguqula indlela yokulawula iziseko zophuhliso.

Yintoni iStackGen?

I-StackGen ngumthombo ovulekileyo, i-cloud-agnostic orchestration platform eyenza ukudalwa kokuziphilisa, ukuzilinganisa, kunye nokuphucula iziseko zeziseko ezingundoqo. Eyakhelwe phezu kwemodyuli, i-architecture eyandisiweyo, i-StackGen ivumela abaphuhlisi ukuba bachaze ulungelelwaniso lweziseko ezingundoqo besebenzisa i-syntax ephezulu, echazayo. Olu lungelelwaniso luthi ke luguqulelwe ekubeni lubonelelo lweziseko ezingundoqo ezisebenzayo nemisebenzi yolawulo, eyenziwa yinjini eyomeleleyo, esasazwayo.

Yintoni iLangChain?

ILangChain yilayibrari yePython ebonelela ngojongano olumanyeneyo lokwakha kunye nokudibanisa iiarhente ze-AI, iimodeli eziveliswayo, kunye namanye amacandelo okufunda koomatshini kwiinkqubo zesoftware. Ngokusebenzisa iLangChain, abaphuhlisi banokwenza izicelo eziphucukileyo eziqhutywa yi-AI ezifunda kwiidatha, zilungelelanise nokutshintsha kweemeko, kwaye zenze izigqibo ezinolwazi. Kwimeko ye-StackGen, i-LangChain yenza ukuhlanganiswa kwee-arhente ze-AI ezinokuhlalutya ukusebenza kweziseko zophuhliso, ukuqikelela ukungahambi kakuhle, kunye nokuthatha amanyathelo okulungisa ukugcina indlela efanelekileyo yokuziphatha.

I-Okhestration yeZiseko eziZimeleyo kunye ne-StackGen kunye neLangChain

Ukubonisa amandla eStackGen kunye neLangChain, makhe siqwalasele umzekelo olula. Masithi sakha iqonga le-e-commerce elisekelwe kwilifu elifuna ukubekelwa bucala, ukusebenza okuphezulu kweziseko ezingundoqo. Ukusebenzisa i-StackGen, sinokuchaza ukucwangciswa kwezinga eliphezulu elichaza i-topology yeziseko ezifunwayo, kubandakanywa nenani leenodi, iindidi zemizekelo, kunye nokucwangciswa kwenethiwekhi.



igama: i-e-commerce-platform
inkcazelo: Scalable e-commerce platform iziseko
inguqulelo: 1.0

amalungu:
  - igama: iseva yewebhu
    uhlobo: EC2
    iipropati:
      umzekelo-uhlobo: t2.micro
      igama lam: ami-abc123
      amaqela okhuseleko: [web-server-sg]
  - igama: isiseko sedatha
    uhlobo: RDS
    iipropati:
      umzekelo-uhlobo: db.t2.micro
      injini: postgres
      Igama lesiseko sedatha: i-e-commerce-db
      Igama lomsebenzisi: i-e-commerce-umsebenzisi
      Igama lokugqitha: e-commerce-password

ubudlelwane:
  -umncedisi wewebhu -> idatabase

Kulo mzekelo, sichaze isitakhi esilula seziseko ezingundoqo esiquka iseva yewebhu kunye nedathabheyisi. Icandelo elithi amacandelo likhankanya amacandelo ngamanye, kuquka iindidi zawo, iipropati, nobudlelwane. Icandelo lobudlelwane lichaza uqhagamshelwano phakathi kwamalungu.

Okulandelayo, siya kusebenzisa iLangChain ukudibanisa i-arhente ye-AI engakwazi ukuhlalutya ukusebenza kweziseko zophuhliso kwaye ithathe amanyathelo okulungisa ukugcina indlela efanelekileyo yokuziphatha. Siza kwenza iarhente ye-AI elula ebeka iliso kusetyenziso lwe-CPU kwaye ihlengahlengise iintlobo zemizekelo ngokufanelekileyo.

# Ikhowudi yearhente yeLangChain AI (ai_agent.py)
ngenisa i-langchain
ukusuka langchain.agents ukungenisa BaseAgent

iklasi ye-ECommerceAgent(BaseAgent):
    def __init__(self, stackgen_config):
        self.stackgen_config = stackgen_config
        self.cpu_utilization_threshold = 80

    def monitor_infrastructure(self):
        # Jonga ukusetyenziswa kweCPU kwaye uhlengahlengise iintlobo zemizekelo
        cpu_usetyenziso = self.get_cpu_utilization()
        ukuba cpu_usetyenziso > self.cpu_utilization_threshold:
            self.adjust_instance_types()

    def adjust_instance_types(self):
        # Lungisa iintlobo zemizekelo esekwe kusetyenziso lweCPU
        example_types = self.get_instance_types()
        umzekelo_chwetheza umzekelo_iintlobo:
            if example_type['cpu_utilization'] > self.cpu_utilization_threshold:
                example_type['instance_type'] = 't2.large'

# Dibanisa iarhente ye-AI kunye neStackGen
stackgen_config = load_yaml('example.yaml')
ai_agent = ECommerceAgent(stackgen_config)
ai_agent.monitor_infrastructure()

Kulo mzekelo, senze i-arhente ye-AI elula ebeka esweni ukusetyenziswa kwe-CPU kwaye ihlengahlengise iindidi zemizekelo ngokufanelekileyo. Sidibanise i-arhente ye-AI kunye ne-StackGen sisebenzisa ithala leencwadi elithi langchain, elibonelela ngojongano olumanyeneyo lokwakha nokudibanisa ii-arhente ze-AI.

Ephezulu 5-10 Eyeyona Isetyenziswayo Imiyalelo/iziqwengana

Nantsi eminye yemiyalelo edla ngokusetyenziswa kunye neziqwengana zeStackGen kunye neLangChain:

  1. IFayile yoLungiselelo lweStackGen: umzekelo we-stackgen config.yaml
  2. LangChain AI Code Agent: langchain ai_agent.py
  3. Dibanisa iArhente ye-AI kunye neStackGen: langchain dibanisa i-ai_agent.py umzekelo.yaml
  4. Beka iziseko zoLwakhiwo: ii-arhente.monitor_infrastructure()
  5. Lungisa Iindidi zemeko: ai_agent.adjust_instance_types()
  6. Fumana usetyenziso lweCPU: ai_agent.get_cpu_utilization()
  7. Fumana Iintlobo zeMibonelo: ai_agent.get_instance_types()
  8. Layisha iFayile yoLungiso yeYAML: load_yaml('example.yaml')
  9. Gcina iFayile yoLungiso yeYAML: gcina_yaml('example.yaml', stackgen_config)
  10. Yibone ngeso lengqondo i-Infrastructure Topology: stackgen bonisa umzekelo.yaml

Umbono

Ukubona i-topology yeziseko ezingundoqo, sinokusebenzisa ithala leencwadi elifana negraphviz ukunika igrafu esekwe kwifayile yoqwalaselo yeStackGen.

{
  "type": "bar",
  "idatha": {
    "labels": ["Web Server", "Database"],
    "iiseti zedatha": [
      {
        "label": "Ukusetyenziswa kweCPU",
        "idatha": [80, 90],
        "backgroundColor": ["rgba(255, 99, 132, 0.2)", "rgba(54, 162, 235, 0.2)"],
        "borderColor": ["rgba(255, 99, 132, 1)", "rgba(54, 162, 235, 1)"],
        "BorderWidth": [1, 1]
      }
    ]
  },
  "iinketho": {
    "isihloko": {
      "bonisa": yinyani,
      "text": "Ukusetyenziswa kweCPU ngamaCandelo"
    },
    "izikali": {
      "yAxes": [
        {
          "bonisa": yinyani,
          "scaleLabel": {
            "bonisa": yinyani,
            "labelString": "Ukusetyenziswa kweCPU (%)"
          }
        }
      ]
    }
  }
}

Le tshati ibona ukusetyenziswa kwe-CPU yomncedisi wewebhu kunye namacandelo edatha, inika imbono ecacileyo yokusebenza kweziseko zophuhliso.

Isiphelo

I-orchestration yeziseko zophuhliso ezizimeleyo kunye ne-StackGen kunye neLangChain yiparadigm enamandla eyenza ukudalwa kwe-self-self-self-scaling, self-scaling, and self-optimisest infrastructure stacks. Ngokusebenzisa ii-arhente ze-AI kunye neemodeli ezivelisayo, abaphuhlisi banokwenza ngokuzenzekelayo kunye nokwandisa ulawulo lweziseko ezingundoqo, ukunciphisa iimpazamo, ukungahambelani, kunye neebhotile. Kule nqaku, sihlolisise iziseko ze-StackGen kunye neLangChain, kubandakanywa nokwakhiwa kwazo, iifayile zokucwangcisa, kunye nokudibanisa i-arhente ye-AI. Sikwanike imizekelo esebenzayo kunye neziqwengana zekhowudi ukubonisa amandla obu buchwepheshe. Njengoko imfuno yolawulo lweziseko ezisebenzayo olusebenzayo kunye ne-scalable iqhubeka ikhula, i-StackGen kunye neLangChain zilungele ukudlala indima ephambili kwixesha elizayo le-DevOps kunye nolawulo lweziseko.

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