Ukuphumeza i-Agenttic AI kwi-DevOps Workflows ye-Autonomous Infrastructure Orchestration | B4N1

Ukuphumeza i-Agenttic AI kwi-DevOps Workflows ye-Autonomous Infrastructure Orchestration

6 months ago · b4n1 Read time: 3 min

Ukuphumeza i-Agenttic AI kwi-DevOps Workflows ye-Autonomous Infrastructure Orchestration

Intshayelelo

Ukudityaniswa kwe-Artificial Intelligence (AI), i-DevOps, kunye ne-orchestration yeziseko ezingundoqo kubangele i-paradigm entsha kulawulo lweziseko ezingundoqo - i-orchestration yeziseko ezizimeleyo. Le ndlela iphakamisa i-AI kunye ne-Machine Learning (ML) i-algorithms ukwenza ngokuzenzekelayo kunye nokwandisa ukunikezelwa kweziseko zophuhliso, ukulinganisa, kunye nokulawula, okwenza abaphuhlisi bagxininise kwimisebenzi yezinga eliphezulu. Kweli nqaku, siza kuphonononga indima ye-agent ye-AI ekuphuculeni amava ophuhlisi kunye nokuphucula ukulinganisa kwi-DevOps workflows, ngokugxila kwizixhobo ezifana neLangChain, DSPy, kunye ne-StackGen.

Yintoni i-Agent AI?

I-Agentic AI ibhekisela kuhlobo lwe-AI olubonisa ukuziphatha okuzimeleyo, ukwenza izigqibo kunye nokuthatha amanyathelo ngokwawo ngaphandle kokungenelela okucacileyo komntu. Kumxholo we-DevOps, i-AI ye-agent ingasetyenziselwa ukuzenzekelayo imisebenzi efana nokubonelela ngeziseko zophuhliso, ukusasazwa, kunye nokulinganisa, kunye nokubeka iliso kunye nokusombulula ingxaki. Le ndlela ivumela abaphuhlisi ukuba bagxininise kwimisebenzi yezinga eliphezulu, njengekhowudi yokubhala kunye nokuyila izicelo, ngelixa inkqubo ye-AI inyamekela iziseko ezisisiseko.

LangChain: Isakhelo soKwakha ii-Agentic AI Systems

ILangChain sisikhokelo somthombo ovulekileyo wokwakha iinkqubo ze-AI ezinokunxibelelana nabasebenzisi, ukwenza imisebenzi, kunye nokwenza izigqibo. Inika isethi yee-APIs kunye nezixhobo zokwakha i-interfaces zencoko, ukusetyenzwa kolwimi lwendalo (NLP), kunye nemodeli yokufunda ngomatshini. I-LangChain ingasetyenziselwa ukwakha uluhlu olubanzi lwezicelo, ukusuka kwii-chatbots kunye nabancedisi benyani ukuya kwiinkqubo zokuzimela zeziseko zophuhliso.

I-DSPy: Ithala leencwadi lePython loKwakha iiNkqubo ze-AI ze-Agenttic

I-DSPy yilayibrari yePython yokwakha iinkqubo ze-AI ezinokusebenzisana nabasebenzisi, benze imisebenzi, kwaye benze izigqibo. Inika isethi yee-APIs kunye nezixhobo zokwakha ujongano lweengxoxo, i-NLP, kunye nemodeli yokufunda koomatshini. I-DSPy ingasetyenziselwa ukwakha uluhlu olubanzi lwezicelo, ukusuka kwii-chatbots kunye nabancedisi benyani ukuya kwiinkqubo zokuzimela zeziseko zophuhliso.

I-StackGen: Isixhobo soKwakha kunye noLawulo lweziSeko ezinguNdoqo

I-StackGen sisixhobo sokwakha nokulawula iziseko zophuhliso njengekhowudi. Inika isethi yee-APIs kunye nezixhobo zokuchaza ukucwangciswa kweziseko zophuhliso, izibonelelo zokubonelela, kunye nokulawula ukuthunyelwa. I-StackGen ingasetyenziselwa ukwakha nokulawula uluhlu olubanzi lweziseko ezingundoqo, ukusuka koomatshini bokwenene kunye nezikhongozeli ukuya kwimisebenzi engenamsebenzi kunye nezicelo ze-cloud-native.

Ukuzalisekisa i-Agent AI kwi-DevOps Workflows

Ukuphumeza i-AI ye-agent kwi-DevOps workflows, sinokusebenzisa indibaniselwano ye-LangChain, i-DSPy, kunye ne-StackGen. Nanku umzekelo wendlela esinokuzisebenzisa ngayo ezi zixhobo ukwakha inkqubo yolawulo lweziseko ezingundoqo ezizimeleyo:

ngenisa i-langchain
ngenisa dSpy
ngenisa i-stackgen


i-infrastructure_config = {
    "imithombo": [
        {"name": "web-server", "type": "EC2", "region": "us-west-2"},
        {"name": "database", "type": "RDS", "region": "us-west-2"}
    ]
}

# Chaza ulungelelwaniso lokusasazwa
deployment_config = {
    "iinkonzo": [
        {"name": "web-server", "image": "nginx:yamva", "port": 80},
        {"name": "database", "image": "mysql:latest", "port": 3306}
    ]
}

# Yenza iarhente yeLangChain
iarhente = langchain.Agent(
    igama = "izixhobo-okhestrator",
    Description="Inkqubo yolawulo lwezakhiwo ezizimeleyo"
)

# Yenza imodeli yeDSPy
imodeli = dspy.Model(
    igama = "izixhobo-okhestrator",
    description="Imodeli yokuqikelela iimfuno zesiseko"
)

# Yenza istaki seStackGen
istaki = stackgen.Stack(
    igama = "izixhobo-okhestrator",
    description="Istakhi sokulawula iziseko zophuhliso"
)

# Chaza ukuhamba komsebenzi kwe-AI yearhente
umsebenzi = [
    {"step": "predict_infrastructure_requirements", "model": model},
    {"step": "provision_infrastructure", "stack": stack},
    {"step": "deploy_services", "deployment_config": deployment_config}
]

# Qhuba ukuhamba komsebenzi kwe-AI
arhente.run(ukuhamba komsebenzi)

Le khowudi ichaza inkqubo ye-orchestration yeziseko ezizimeleyo esebenzisa i-LangChain, i-DSPy, kunye ne-StackGen ukuqikelela iimfuno zeziseko zophuhliso, izixhobo zokubonelela, kunye nokuthunyelwa kweenkonzo.

Ephezulu 5-10 Eyeyona Isetyenziswayo Imiyalelo/iziqwengana

Nantsi eyona miyalelo/iziqwengana eziphezulu ezi-5-10 zeLangChain, DSPy, kunye neStackGen:

Isixhobo Umyalelo/Isiqwenga Inkcazelo
LangChain langchain.Agent.dala() Yenza iarhente entsha yeLangChain
LangChain langchain.Agent.describe() Ibuyisela inkcazo ye-arhente ye-LangChain
DSPy dspy.Model.dala() Yenza imodeli entsha ye-DSPy
DSPy dspy.Model.describe() Ibuyisela inkcazo yemodeli ye-DSPy
StackGen stackgen.Stack.create() Yenza isitakhi esitsha seStackGen
StackGen stackgen.Stack.describe() Ibuyisela inkcazo yestackGen
LangChain langchain.Agent.run() Iqhuba iarhente yeLangChain
DSPy dspy.Model.predict() Uqikelela iimfuno zeziseko zophuhliso usebenzisa imodeli ye-DSPy
StackGen stackgen.Stack.provision() Izibonelelo zezibonelelo zisebenzisa iStackGen stack
StackGen stackgen.Stack.deploy() Usebenzisa iinkonzo kusetyenziswa isitaki se-StackGen

Umbono

Nantsi umfanekiso wokuhamba komsebenzi we-AI usebenzisa iChart.js:

{
  "type": "bar",
  "idatha": {
    "labels": ["Cela iiMfuno zeSakhiwo esiqikelelwayo", "Ubonelelo lweziSeko ezinguNdoqo", "Beka iiNkonzo"],
    "iiseti zedatha": [
      {
        "label": "Agentic AI Workflow",
        "idatha": [10, 20, 30],
        "backgroundColor": "rgba(255, 99, 132, 0.2)",
        "borderColor": "rgba(255, 99, 132, 1)",
        "BorderWidth": 1
      }
    ]
  },
  "iinketho": {
    "isihloko": {
      "bonisa": yinyani,
      "text": "Agentic AI Workflow"
    },
    "izikali": {
      "yAxes": [
        {
          "amanqaku": {
            "beginAtZero": yinyani
          }
        }
      ]
    }
  }
}

Oku kubonwa kubonisa ukuhamba komsebenzi kwe-AI ye-agetic njengetshathi yebha, kunye namanyathelo amathathu okuqikelela iimfuno zeziseko zophuhliso, ukubonelela ngeziseko zophuhliso, kunye nokuthunyelwa kweenkonzo.

Isiphelo

Kweli nqaku, siphonononge indima ye-AI ye-arhente ekuphuculeni amava omphuhlisi kunye nokuphucula ukulinganisa kwi-DevOps workflows. Sazisa i-LangChain, i-DSPy, kunye ne-StackGen njengezixhobo zokwakha iinkqubo ze-AI ze-agent, kwaye sabonisa indlela yokusebenzisa ezi zixhobo ukwakha inkqubo yokuzimela yeziseko zophuhliso. Sikwabonelela ngokubonwa kwe-agenti ye-AI yokuhamba komsebenzi usebenzisa iChart.js. Ngokusebenzisa i-AI ye-agetic, abaphuhlisi banokugxila kwimisebenzi yezinga eliphezulu, njengekhowudi yokubhala kunye nokuyila izicelo, ngelixa inkqubo ye-AI inyamekela iziseko ezisisiseko.


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