Layisha ukucushwa komenzeli we-AWS DevOps

Ukuphenya Kwesigameko Okuzenzakalelayo Ngama-AI-Powered Agents kanye Namathuluzi we-DevOps

Isingeniso

Ezimweni zanamuhla zamafu, uphenyo lwezigameko nokulungiswa kuyizici ezibalulekile zemikhuba ye-DevOps. Kodwa-ke, uphenyo lwesigameko olwenziwa mathupha lungadla isikhathi, lube namaphutha, futhi ngokuvamile ludinga ubuchwepheshe obuningi. Ukuze sibhekane nalezi zinselele, sizohlola ukuhlanganiswa kwama-ejenti anamandla e-Kiro AI ne-AWS DevOps Agent kanye ne-DSPy ukuze kuphenywe izigameko ezizenzakalelayo nokuxazululwa.

Ukuqonda Izingxenye

Ngaphambi kokungena ekuhlanganiseni, ake siqonde izingxenye ezihilelekile:

Ama-ejenti Ane-Kiro AI-Powered: I-Kiro iyinkundla ye-AI-powered evumela ukudalwa kwama-ejenti angokwezifiso wokuzenzela imisebenzi nokugeleza komsebenzi. Laba basebenzeli bangaqeqeshwa emithonjeni ehlukahlukene yedatha, okuhlanganisa amalogi, amamethrikhi, namafayela okulungiselela.
I-AWS DevOps Agent: I-AWS DevOps Agent iyithuluzi elinika amandla ukuzenzekela kwemisebenzi nokugeleza komsebenzi ku-AWS. Ihlinzeka ngendlela eguquguqukayo nenwebekayo yokwenza imisebenzi ngokuzenzakalelayo, njengokuphakela izinhlelo zokusebenza, ukuqapha ukusebenza, nezinkinga zokuxazulula izinkinga.
I-DSPy: I-DSPy umtapo wezincwadi wePython ohlinzeka ngendlela elula nephumelelayo yokusebenzisana ne-DSPy, inkundla yedatha etholakala emafini. I-DSPy inika amandla ukudalwa kwamapayipi edatha angokwezifiso, ukucutshungulwa kwedatha, nokubonwa kwedatha.

Uhlolojikelele Lokuhlanganisa

Ukuhlanganiswa kwama-ejenti anamandla e-Kiro AI ne-AWS DevOps Agent kanye ne-DSPy kuhilela izinyathelo ezilandelayo:

Ukuqoqwa Kwedatha: Umenzeli we-AWS DevOps uqoqa idatha emithonjeni ehlukahlukene, njengamalogu, amamethrikhi, namafayela okulungiselela.
Ukucutshungulwa Kwedatha: Idatha eqoqiwe icutshungulwa kusetshenziswa i-DSPy, evumela ukudalwa kwamapayipi edatha angokwezifiso, ukucutshungulwa kwedatha, nokubonwa kwedatha.
Ukuqeqeshwa Komenzeli: Idatha ecutshunguliwe isetshenziselwa ukuqeqesha umenzeli onikwe amandla we-Kiro AI, okuvumela umenzeli ukuthi afunde amaphethini nokudidayo kudatha.
Uphenyo Lwesigameko: Uma kwenzeka isigameko, i-ejenti ye-Kiro AI-powered iqala ukuphenya isigameko isebenzisa amamodeli aqeqeshiwe kanye nedatha.
Isinqumo: Umenzeli unikeza izincomo zokuxazulula isigameko, ezingenziwa kusetshenziswa i-AWS DevOps Agent.

Isibonelo sekhodi

Nasi isibonelo sekhodi esilula esibonisa ukuhlanganiswa kwama-ejenti anamandla e-Kiro AI ne-AWS DevOps Agent kanye ne-DSPy:

ngenisa os
ngenisa json
kusuka kumenzeli wokungenisa we-kiros
kusuka ku-dspy yokungenisa i-DSPy


aws_config = json.load(open('aws_config.json'))

# Dala i-ejenti enamandla ye-Kiro AI
i-ejenti = i-ejenti (
    igama='uphenyo_lwesigameko',
    description='Uphenyo nokuxazulula isigameko esizenzakalelayo',
    imodeli='imodeli_yophenyo_yesigameko'
)

# Dala isibonelo se-DSPy
dspy = DSPy(
    umsingathi='umgcini wendawo',
    port=8080,
    igama lomsebenzisi='admin',
    iphasiwedi='iphasiwedi'
)

# Chaza ipayipi ledatha usebenzisa i-DSPy
iphayiphi = dspy.pipeline(
    name='incident_investigation_pipeline',
    description='Ipayipi lokuphenya izigameko ezizenzakalelayo',
    imithombo=['amalogi', 'metrics', 'configuration']
)

# Qeqesha i-ejenti ene-Kiro AI usebenzisa ipayipi ledatha
i-agent.train(
    iphayiphi=ipayipi,
    idatha=dspy.data(
        igama='idatha_yophenyo_yesigameko',
        description='Idatha yophenyo lwesigameko esizenzakalelayo'
    )
)

# Qalisa i-ejenti ye-Kiro AI-powered ukuphenya isigameko
isigameko = {
    'id': 'INC-12345',
    'incazelo': 'Isikhathi sokuphumula sohlelo lokusebenza'
}
i-agent.trigger(
    isigameko=isigameko,
    idatha=dspy.data(
        igama='idatha_yophenyo_yesigameko',
        description='Idatha yophenyo lwesigameko esizenzakalelayo'
    )
)

# Thola izincomo ze-ejenti zokuxazulula isigameko
izincomo = agent.recommendations(
    isigameko=isigameko,
    idatha=dspy.data(
        igama='idatha_yophenyo_yesigameko',
        description='Idatha yophenyo lwesigameko esizenzakalelayo'
    )
)

# Yenza izincomo zomenzeli usebenzisa i-AWS DevOps Agent
aws_config['izincomo'] = izincomo
aws_config['incident_id'] = isigameko['id']
aws_config['description'] = isigameko['incazelo']

Imiyalo/Amazwibela Aphezulu Okuyi-5-10

Nayi imiyalo/ amazwibela aphezulu angu-5-10 alobu buchwepheshe:

I-Kiro AI-Powered Agent Creation:

i-ejenti = i-ejenti ( igama='uphenyo_lwesigameko', description='Uphenyo nokuxazulula isigameko esizenzakalelayo', imodeli='imodeli_yophenyo_yesigameko' )

2. **I-DSPy Instance Creation**:
    ``` inhlwathi
dspy = DSPy(
    umsingathi='umgcini wendawo',
    port=8080,
    igama lomsebenzisi='admin',
    iphasiwedi='iphasiwedi'
)

Incazelo Yephayiphi Yedatha:

iphayiphi = dspy.pipeline( name='incident_investigation_pipeline', description='Ipayipi lokuphenya izigameko ezizenzakalelayo', imithombo=['amalogi', 'metrics', 'configuration'] )

4. **Ukuqeqeshwa Komenzeli Onamandla we-Kiro AI**:
    ``` inhlwathi
i-agent.train(
    iphayiphi=ipayipi,
    idatha=dspy.data(
        igama='idatha_yophenyo_yesigameko',
        description='Idatha yophenyo lwesigameko esizenzakalelayo'
    )
)

I-Kiro AI-Powered Agent Triggering:

i-agent.trigger( isigameko=isigameko, idatha=dspy.data( igama='idatha_yophenyo_yesigameko', description='Idatha yophenyo lwesigameko esizenzakalelayo' ) )

6. **Ukuthola Izincomo Zomenzeli**:
    ``` inhlwathi
izincomo = agent.recommendations(
    isigameko=isigameko,
    idatha=dspy.data(
        igama='idatha_yophenyo_yesigameko',
        description='Idatha yophenyo lwesigameko esizenzakalelayo'
    )
)

I-AWS DevOps Configuration Agent Iyalayisha:

aws_config = json.load(open('aws_config.json'))

8. **Ukwenziwa Kwesincomo Somenzeli we-AWS DevOps**:
    ``` inhlwathi
aws_config['izincomo'] = izincomo
aws_config['incident_id'] = isigameko['id']
aws_config['description'] = isigameko['incazelo']

Ukubuyiswa Kwedatha ye-DSPy:

idatha = dspy.data( igama='idatha_yophenyo_yesigameko', description='Idatha yophenyo lwesigameko esizenzakalelayo' )

10. **Imodeli Yomenzeli Onamandla we-Kiro AI Iyalayisha**:
    ``` inhlwathi
imodeli = i-agent.model(
    igama='imodeli_yophenyo_yesigameko',
    description='Imodeli yophenyo yesigameko esizenzakalelayo'
)

Ukubona

Nakhu okubonwayo kokuhlanganiswa kusetshenziswa i-Chart.js:

{
  "type": "bar",
  "idatha": {
    "labels": ["Ukuqoqwa Kwedatha", "Ukucutshungulwa Kwedatha", "Ukuqeqeshwa Komenzeli", "Uphenyo Lwesigameko", "Isixazululo"],
    "idathasethi": [
      {
        "label": "Isikhathi (amasekhondi)",
        "idatha": [10, 20, 30, 40, 50],
        "Umbala wangemuva": [
          "rgba(255, 99, 132, 0.2)",
          "rgba(54, 162, 235, 0.2)",
          "rgba(255, 206, 86, 0.2)",
          "rgba(75, 192, 192, 0.2)",
          "rgba(153, 102, 255, 0.2)"
        ],
        "Umbala Womngcele": [
          "rgba(255, 99, 132, 1)",
          "rgba(54, 162, 235, 1)",
          "rgba(255, 206, 86, 1)",
          "rgba(75, 192, 192, 1)",
          "rgba(153, 102, 255, 1)"
        ],
        "BorderWidth": 1
      }
    ]
  },
  "izinketho": {
    "izikali": {
      "yAxes": [
        {
          "imikhaza": {
            "beginAtZero": kuyiqiniso
          }
        }
      ]
    }
  }
}

Lokhu kubukwa kukhombisa isikhathi esithathiwe esinyathelweni ngasinye kunqubo yokuhlanganisa.

Isiphetho

Kulesi sihloko, sihlole ukuhlanganiswa kwama-ejenti anamandla e-Kiro AI ne-AWS DevOps Agent kanye ne-DSPy ukuze kuphenywe izigameko ezizenzakalelayo nokuxazululwa ezindaweni ezihlala amafu. Sinikeze umbono oningiliziwe wezingxenye ezihilelekile, inqubo yokuhlanganisa, kanye nesibonelo esilula sekhodi esibonisa ukuhlanganiswa. Siphinde safaka ukubonwa kokuhlanganiswa kusetshenziswa i-Chart.js kanye nohlu lwemiyalo/ amazwibela asetshenziswa kakhulu angu-5-10 kulobu buchwepheshe.

Layisha ukucushwa komenzeli we-AWS DevOps

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