Ifayela lokumisa le-StackGen (example.yaml)

I-Autonomous Infrastructure Orchestration ene-StackGen kanye ne-LangChain: I-Leveraging AI Agents kanye namamodeli akhiqizayo we-Extreme DevOps Automation kanye Nokuphathwa Kwengqalasizinda Ezizimele

Isingeniso

Kusimo sedijithali esisheshayo sanamuhla, isidingo sokuphathwa kwengqalasizinda esebenzayo nenyukayo asikaze sibe phezulu. Imikhuba ye-DevOps evamile ivamise ukuncika ekucushweni ngesandla nasekubhalweni, okungaholela kumaphutha, ukungahambisani, kanye nezingqinamba. Ukuze kubhekwane nalezi zinselele, sibona ukuvela kwe-orchestration yengqalasizinda ezimele, ipharadigm esebenzisa ama-agent e-AI namamodeli akhiqizayo ukuze enze ngokuzenzakalelayo futhi athuthukise ukuphathwa kwengqalasizinda. Kulesi sihloko, sizongena emhlabeni othokozisayo we-StackGen kanye ne-LangChain, ubuchwepheshe obubili obuphambili obushintsha indlela esiphatha ngayo ingqalasizinda.

Yini i-StackGen?

I-StackGen iwumthombo ovulekile, inkundla yengqalasizinda ye-cloud-agnostic evumela ukuthi kusungulwe izitaki zengqalasizinda eziziphilisayo, zokuzilinganisa, kanye nokuzilungiselela ngokwakho. Yakhelwe phezu kwe-modular, i-architecture enwebekayo, i-StackGen ivumela abathuthukisi ukuthi bachaze ukucushwa kwengqalasizinda besebenzisa i-syntax yezinga eliphezulu, echazayo. Le syntax ibe isihunyushwa emisebenzini yokuhlinzeka nengqalasizinda esebenzayo kanye nokuphatha, eyenziwa injini eqinile, esabalalisiwe.

Iyini i-LangChain?

I-LangChain iwumtapo wezincwadi wePython ohlinzeka ngesixhumi esibonakalayo esihlanganisiwe sokwakha nokuhlanganisa ama-agent e-AI, amamodeli akhiqizayo, nezinye izingxenye zokufunda zomshini ezinhlelweni zesofthiwe. Ngokusebenzisa i-LangChain, abathuthukisi bangakha izinhlelo zokusebenza ezithuthukisiwe eziqhutshwa yi-AI ezifunda kudatha, zivumelane nezimo ezishintshayo, futhi zenze izinqumo ezinolwazi. Ngomongo we-StackGen, i-LangChain inika amandla ukuhlanganiswa kwama-agent e-AI angakwazi ukuhlaziya ukusebenza kwengqalasizinda, abikezele okungahambi kahle, futhi athathe izinyathelo zokulungisa ukuze kugcinwe ukuziphatha okuhle kwesistimu.

I-Autonomous Infrastructure Orchestration ene-StackGen ne-LangChain

Ukukhombisa amandla e-StackGen ne-LangChain, ake sicabangele isibonelo esilula. Ake sithi sakha inkundla ye-e-commerce esekelwe emafini edinga isitaki sengqalasizinda esikalayo, esisebenza kahle. Sisebenzisa i-StackGen, singachaza ukucushwa kwezinga eliphezulu okucacisa i-topology yengqalasizinda efiselekayo, okuhlanganisa nenani lamanodi, izinhlobo zezibonelo, nokulungiselelwa kwenethiwekhi.



Igama: i-e-commerce-platform
incazelo: Ingqalasizinda yesikhulumi se-e-commerce esheshayo
inguqulo: 1.0

izingxenye:
  - Igama: iseva yewebhu
    uhlobo: EC2
    izakhiwo:
      isibonelo-uhlobo: t2.micro
      mina: ami-abc123
      amaqembu okuphepha: [web-server-sg]
  - Igama: database
    uhlobo: RDS
    izakhiwo:
      isibonelo-uhlobo: db.t2.micro
      injini: postgres
      Igama lesizindalwazi: i-e-commerce-db
      igama lomsebenzisi: i-e-commerce-user
      iphasiwedi: e-commerce-password

ubudlelwano:
  - iseva yewebhu -> isizindalwazi

Kulesi sibonelo, sichaze isitaki sengqalasizinda esilula esihlanganisa iseva yewebhu kanye nesizindalwazi. Isigaba sezingxenye sicacisa izingxenye ezingazodwana, okuhlanganisa izinhlobo zazo, izici, nobudlelwano. Isigaba sobudlelwano sichaza ukuxhumana phakathi kwezingxenye.

Okulandelayo, sizosebenzisa i-LangChain ukuhlanganisa i-ejenti ye-AI engahlaziya ukusebenza kwengqalasizinda futhi ithathe izinyathelo zokulungisa ukuze kugcinwe ukuziphatha okuhle kwesistimu. Sizodala i-ejenti ye-AI elula eqapha ukusetshenziswa kwe-CPU futhi silungise izinhlobo zezibonelo ngokufanele.

# Ikhodi ye-ejenti ye-LangChain AI (ai_agent.py)
ngenisa i-langchain
kusuka ku-langchain.agents ingenisa i-BaseAgent

isigaba se-ECommerceAgent(BaseAgent):
    def __init__(self, stackgen_config):
        self.stackgen_config = stackgen_config
        self.cpu_utilization_threshold = 80

    def monitor_infrastructure(self):
        # Bheka ukusetshenziswa kwe-CPU futhi ulungise izinhlobo zezibonelo
        cpu_utilization = self.get_cpu_utilization()
        uma cpu_utilization > self.cpu_utilization_threshold:
            self.adjust_instance_types()

    def adjust_instance_types(self):
        # Lungisa izinhlobo zezibonelo ngokusekelwe ekusetshenzisweni kwe-CPU
        example_types = self.get_instance_types()
        isibonelo_thayipha ku-example_types:
            if example_type['cpu_utilization'] > self.cpu_utilization_threshold:
                example_type['instance_type'] = 't2.large'

# Hlanganisa i-ejenti ye-AI ne-StackGen
stackgen_config = load_yaml('example.yaml')
i-ai_agent = ECommerceAgent(stackgen_config)
i-ai_agent.monitor_infrastructure()

Kulesi sibonelo, sidale i-ejenti ye-AI elula eqapha ukusetshenziswa kwe-CPU futhi ilungise izinhlobo zezibonelo ngokufanele. Sihlanganise i-ejenti ye-AI ne-StackGen sisebenzisa umtapo langchain, ohlinzeka ngesixhumi esibonakalayo esihlanganisiwe sokwakha nokuhlanganisa ama-agent e-AI.

Imiyalo/Amazwibela Aphezulu Okuyi-5-10

Nansi eminye yemiyalo evame ukusetshenziswa kakhulu namazwibela we-StackGen ne-LangChain:

Ifayela Lokucushwa Le-StackGen: i-stackgen config example.yaml
Ikhodi yomenzeli we-LangChain AI: langchain ai_agent.py
Hlanganisa i-AI Agent ne-StackGen: langchain integrate ai_agent.py example.yaml
Qapha Ingqalasizinda: ai_agent.monitor_infrastructure()
Lungisa Izinhlobo Zesimo: ai_agent.adjust_instance_types()
Thola Ukusetshenziswa kwe-CPU: ai_agent.get_cpu_utilization()
Thola Izinhlobo Zesimo: ai_agent.get_instance_types()
Layisha Ifayela Lokucupha le-YAML: load_yaml('example.yaml')
Londoloza Ifayela Lokucupha le-YAML: gcina_yaml('example.yaml', stackgen_config)
Bona ngeso lengqondo I-Topology Yengqalasizinda: i-stackgen bona ngeso lengqondo isibonelo.yaml

Ukubona

Ukuze ubone ngeso lengqondo i-topology yengqalasizinda, singasebenzisa umtapo wezincwadi ofana negraphviz ukuze sinikeze igrafu ngokusekelwe kufayela lokumisa le-StackGen.

{
  "type": "bar",
  "idatha": {
    "labels": ["Web Server", "Database"],
    "idathasethi": [
      {
        "label": "Ukusetshenziswa kwe-CPU",
        "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]
      }
    ]
  },
  "izinketho": {
    "isihloko": {
      "Display": iqiniso,
      "text": "Ukusetshenziswa kwe-CPU Ngengxenye"
    },
    "izikali": {
      "yAxes": [
        {
          "Display": iqiniso,
          "scaleLabel": {
            "Display": iqiniso,
            "labelString": "Ukusetshenziswa kwe-CPU (%)"
          }
        }
      ]
    }
  }
}

Leli shadi libonisa ukusetshenziswa kwe-CPU kweseva yewebhu kanye nezingxenye zesizindalwazi, linikeza umbono ocacile wokusebenza kwengqalasizinda.

Isiphetho

Ukuhlelwa kwezingqalasizinda ezizimele nge-StackGen kanye ne-LangChain ipharadigm enamandla eyenza ukuthi kusungulwe izitaki zengqalasizinda eziziphilisayo, zokuzilinganisa, kanye nokuzilungiselela ngokwakho. Ngokusebenzisa ama-agent e-AI namamodeli akhiqizayo, abathuthukisi bangakwazi ukwenza ngokuzenzakalelayo futhi bathuthukise ukuphathwa kwengqalasizinda, behlise amaphutha, ukungahambisani, kanye nezingqinamba. Kulesi sihloko, sihlole izinto eziyisisekelo ze-StackGen ne-LangChain, okuhlanganisa ukwakheka kwazo, amafayela okumisa, nokuhlanganiswa kwe-ejenti ye-AI. Siphinde sanikeza izibonelo ezingokoqobo namazwibela ekhodi ukukhombisa amandla alobu buchwepheshe. Njengoba isidingo sokuphathwa kwengqalasizinda ephumelelayo nenyukayo siqhubeka sikhula, i-StackGen ne-LangChain zikulungele ukudlala indima ehamba phambili esikhathini esizayo se-DevOps nokuphathwa kwengqalasizinda.

Ifayela lokumisa le-StackGen (example.yaml)

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