Fausia se sui Kiro | B4N1

Fausia se sui Kiro

8 months ago · b4n1 Read time: 5 min

ZeroOps DevOps Transformation ma AI-Powered Agents and Orchestration

Fa'atomuaga

O le DevOps revolution ua suia le auala e atiaʻe ai, faʻapipiʻi, ma tausia le polokalama. Ae ui i lea, o le auala masani a DevOps e manaʻomia ai le tele o taumafaiga tusi lesona, tomai, ma le taimi. O le tulaʻi mai o sui faʻamalosi AI ma meafaigaluega faʻapipiʻi ua mafai ai ona faʻaogaina faiga DevOps, e taʻitaʻia ai le manatu o ZeroOps. I totonu o lenei tusiga, o le a matou suʻesuʻeina le faʻatinoga o le faʻaogaina o le DevOps faʻaogaina AI e faʻaaoga ai Kiro sui ma meafaigaluega faʻapipiʻi e pei o LangChain, faʻatasi ai ma le taulai atu i le suia o DevOps faiga i ZeroOps.

O le a le ZeroOps?

ZeroOps o se manatu faʻaalia i DevOps o loʻo faʻamoemoe e faʻaumatia le manaʻoga mo le faʻaogaina o le tusi lesona i le atinaʻeina ma le faʻaogaina o le faagasologa. E ala i le faʻaogaina o le AI ma le aʻoaʻoina o masini, ZeroOps e mafai ai ona faʻapipiʻiina otometi, faʻavasegaina, ma le mataʻituina o talosaga, faʻaitiitia le lamatiaga o mea sese a tagata ma faʻateleina le lelei. O le sini autu a ZeroOps o le fatuina lea o se faʻamalologa a le tagata lava ia, faʻaleleia e le tagata lava ia, ma le faʻaogaina o le tagata lava ia e manaʻomia ai le faʻaogaina o tagata.

AI-Powered Agents: Le Ki i ZeroOps

AI-powered agents o polokalama faakomepiuta e faʻaogaina ai masini aʻoaʻoina algorithms e faʻautometi ai galuega ma fai filifiliga. I le tulaga o DevOps, AI-powered agents e mafai ona faʻaogaina e mataʻituina ai le faʻatinoga o talosaga, iloa mea faʻaletonu, ma faia faʻatonuga. Kiro agents o se ituaiga o AI-powered agent e mafai ona faʻaogaina mo DevOps automation.

Kiro Agents

Kiro agents o se ituaiga o AI-powered agent e mafai ona faʻaogaina mo DevOps automation. Ua mamanuina e galulue faatasi ma meafaigaluega faʻapipiʻi e pei o LangChain e faʻatautaia le faʻaogaina, faʻavasegaina, ma le mataʻituina o talosaga. E mafai ona a'oa'oina sui o le Kiro i fa'amaumauga fa'asolopito e a'oa'o ai fa'ata'ita'iga ma fa'aletonu, e mafai ai ona latou faia fa'ai'uga fa'apitoa ma faia fa'atonuga.

LangChain: Le Meafaigaluega Fa'asologa

LangChain o se meafaigaluega faʻapipiʻi e mafai ai ona faʻaogaina le faʻagasologa o DevOps e faʻaaoga ai sui faʻamalosi AI e pei o Kiro. O loʻo tuʻuina atu ai se faʻavae mo le fausiaina ma le faʻaogaina o sui faʻamalosia AI e mafai ona faʻaogaina galuega e pei o le faʻapipiʻiina, faʻavasegaina, ma le mataʻituina. E lagolagoina e LangChain le tele o gagana polokalame, e aofia ai le Python, Rust, ma le Go.

** Faʻatinoina AI-Powered DevOps Automation ma Kiro ma LangChain**

Ina ia faʻatinoina le faʻaogaina o le DevOps faʻaogaina AI e faʻaaoga ai Kiro ma LangChain, e tatau ona tatou mulimuli i laasaga nei:

  1. ** Faʻapipiʻi Kiro ma LangChain **: Faʻapipiʻi Kiro ma LangChain e faʻaaoga ai le pip poʻo lau pule o pusa e manaʻomia.
  2. Fausia se Kiro Agent: Fausia se sui Kiro e mafai ona faʻaoga mo DevOps masini. E aofia ai le fa'amalamalamaina o le amio a le sooupu, e pei o le mata'ituina o le fa'atinoga o le talosaga ma le su'eina o fa'aletonu.
  3. A'oa'o le Kiro Agent: A'oa'o le sui o le Kiro i fa'amaumauga fa'asolopito e a'oa'o ai mamanu ma fa'aletonu. Ole mea lea e mafai ai e le sooupu ona faia fa'ai'uga fa'apitoa ma faia fa'atonuga.
  4. ** Faʻatasi ma LangChain **: Faʻafesoʻotaʻi le sui Kiro ma LangChain e faʻautometi faiga DevOps. E aofia ai le faʻamalamalamaina o le faʻasologa o galuega ma le faʻatulagaina o le sooupu e faʻatino galuega.

** Fa'ata'ita'iga Fa'ailoga: Fausia o se Kiro Agent**

O se faʻataʻitaʻiga faʻailoga poloka i le Python e fatuina ai se sui Kiro:

kiro mai fafo
mai langchain faaulufale mai LLMS


sooupu = kiro.Agent(
    igoa="my_agent",
    amio={
        "monitor_performance": {
            "function": "monitor_performance",
            "args": ["app_name", "threshold"],
        },
        "su'esu'e_anomaly": {
            "function": "su'esu'e_anomaly",
            "args": ["app_name", "threshold"],
        },
    },
)

# Aʻoaʻo le sooupu i faʻamatalaga faʻasolopito
agent.train(
    fa'amaumauga=[
        {"app_name": "my_app", "performance": 90},
        {"app_name": "my_app", "performance": 80},
        {"app_name": "my_app", "performance": 70},
    ],
    paepae=85,
)

# Faʻatasi ma LangChain
llms = LLMS(
    sui = sooupu,
    galuega fa'atino={
        "monitor_performance": {
            "function": "monitor_performance",
            "args": ["my_app", 85],
        },
        "su'esu'e_anomaly": {
            "function": "su'esu'e_anomaly",
            "args": ["my_app", 85],
        },
    },
)

# Faʻatino le faʻasologa o galuega
llms.execute()

O lenei tulafono e fausia ai se sui Kiro e mataʻituina le faʻatinoga o talosaga ma iloa ai mea faʻaletonu. Ona aʻoaʻoina lea o le sooupu i luga o faʻamaumauga faʻasolopito ma tuʻufaʻatasia ma LangChain e faʻaogaina ai DevOps faiga.

Poloaiga/Snippets pito i luga 5-10 Fa'aaogaina

O le pito i luga 5-10 pito sili ona faʻaaogaina poloaiga / snippets mo Kiro ma LangChain:

  1. Fausia se Kiro Agent: kiro.Agent(name="my_agent", behavior={"monitor_performance": {"function": "monitor_performance", "args": ["app_name", "threshold"]}})
  2. Toleniina le Kiro Agent: agent.train(data=[{"app_name": "my_app", "performance": 90}], threshold=85)
  3. Fa'atasi ma LangChain: llms = LLMS(agent=agent, workflow={"monitor_performance": {"function": "monitor_performance", "args": ["my_app", 85]}})
  4. Fa'atino le Fa'asologa o Galuega: llms.execute()
  5. Fa'amalamalamaina o le Fa'asologa o Galuega: workflow = {"monitor_performance": {"function": "monitor_performance", "args": ["my_app", 85]}}
  6. Fa'atonu le Sui e Fa'atino Galuega: agent.configure({"function": "execute_task", "args": ["task_name"]})
  7. ** Mata'ituina le Fa'atinoga o Talosaga**: agent.monitor_performance(app_name="my_app", threshold=85)
  8. Su'esu'eina Fa'aletonu: agent.detect_anomaly(app_name="my_app", threshold=85)
  9. Mauaina le Tulaga o le Agent: agent.status()
  10. Mauaina Fa'amaumauga a le Agent: agent.logs()

Va'aiga

Ole fa'aaliga lea ole paipa DevOps ile fa'aogaina ole Chart.js:

{
  "type": "pa",
  "fa'amatalaga": {
    "labels": ["Monitor Performance", "Su'esu'e Anomaly", "Fa'atino Galuega"],
    "fa'amaumauga": [
      {
        "label": "Agent",
        "faamatalaga": [90, 80, 70],
        "backgroundColor": "rgba(255, 99, 132, 0.2)",
        "borderColor": "rgba(255, 99, 132, 1)",
        "BorderWidth": 1
      }
    ]
  },
  "filifiliga": {
    "igoa": {
      "fa'aaliga": moni,
      "text": "DevOps Pipeline"
    },
    "fua": {
      "yAxes": [
        {
          "tiki": {
            "beginAtZero": moni
          }
        }
      ]
    }
  }
}

O lenei faʻaaliga o loʻo faʻaalia ai le paipa o le DevOps ma vaega e tolu: Mataʻituina le Faʻatinoga, Suʻe Anomaly, ma Faʻatino Galuega. O le fa'atinoga a le sooupu o lo'o fa'aalia o se siata pa.

Fa'ai'uga

I totonu o lenei tusiga, na matou suʻesuʻeina le faʻatinoga o le faʻaogaina o le DevOps faʻaogaina AI e faʻaaoga ai Kiro sui ma meafaigaluega faʻapitoa e pei o LangChain. Na matou faia se sui Kiro e mataʻituina le faʻatinoga o talosaga ma iloa ai mea faʻaletonu, ma tuʻufaʻatasia i le LangChain e faʻatometi faiga DevOps. Na matou tuʻuina atu foʻi se faʻaaliga o le DevOps pipeline e faʻaaoga ai le Chart.js. O le sini autu a ZeroOps o le fatuina lea o se faʻamalologa a le tagata lava ia, faʻaleleia e le tagata lava ia, ma le faʻaogaina o le tagata lava ia e manaʻomia ai le faʻaogaina o tagata. E ala i le faʻaaogaina o le AI ma le aʻoaʻoina o masini, e mafai ona tatou ausia lenei sini ma suia faiga DevOps i ZeroOps.

Have Questions?

Let's Talk

Contact me on LinkedIn