Ukulungelelanisa i-Agenttic AI kwiMibhobho ye-DevOps ene-DSPy kunye neLangChain yoPhuculo lweZeroOps

Ukucwangcisa i-Agenttic AI kwiMibhobho ye-DevOps ene-DSPy kunye neLangChain yoPhuculo lweZeroOps

Intshayelelo

Ukudityaniswa kwe-Artificial Intelligence (AI) kwimibhobho ye-DevOps iye yaguqula indlela isoftware ephuhliswa ngayo, ivavanywa, kwaye isetyenziswe. I-Agentic AI, i-subset ye-AI eyenza ukuzimela kwezigqibo kunye nokuthatha amanyathelo, inamandla okuphucula ngakumbi ukusebenza kakuhle kunye nokusebenza kweenkqubo ze-DevOps. Kweli nqaku, siza kuphonononga ingcamango ye-orchestrating agent AI kwimibhobho ye-DevOps usebenzisa i-DSPy kunye neLangChain, kwaye sixoxe ngeenzuzo zale ndlela ekufezekiseni iZeroOps.

Yintoni i-Agent AI?

I-Agent AI ibhekisa kuhlobo lwe-AI olunamandla okwenza izigqibo kunye nokuthatha amanyathelo ngokuzimeleyo, ngaphandle kokungenelela komntu. Oku kuphunyezwa ngokusetyenziswa komgangatho wokufunda koomatshini, ukusetyenzwa kolwimi lwendalo, kunye nolwakhiwo lokuqonda. Iinkqubo ze-AI ze-Agent zinokufunda kumava, ziqhelane neemeko ezintsha, kwaye zinxibelelane nabantu ngendlela yendalo kunye ne-intuitive.

Yintoni iZeroOps?

I-ZeroOps yifilosofi ye-DevOps ejolise ekupheliseni imfuno yokungenelela kwabantu kuphuhliso lwesoftware, ukuthunyelwa kunye nokugcinwa. Oku kuphunyezwa ngokusetyenziswa kwe-automation, orchestration, kunye nokwenza izigqibo eziqhutywa yi-AI. I-ZeroOps yenza ukuba imibutho isebenzise isoftware ngokukhawuleza, enomgangatho ophezulu, nangeendleko eziphantsi.

Ukucwangcisa iAgenttic AI kwiMibhobho yeDevOps

Ukucwangcisa i-AI ye-arhente kwimibhobho ye-DevOps, kufuneka sidibanise ukwenza izigqibo eziqhutywa yi-AI kunye nokuthatha amanyathelo kumbhobho. Oku kunokufezekiswa ngokusetyenziswa kwe-DSPy kunye neLangChain, izikhokelo ezibini ezidumileyo zokwakha kunye nokuthunyelwa kwezicelo eziqhutywa yi-AI.

DSPy

I-DSPy sisikhokelo sePython sokwakha kunye nokuthunyelwa kwezicelo eziqhutywa yi-AI. Ibonelela ngeseti yezixhobo kunye namathala eencwadi okulungiswa kwedatha, ukufundwa koomatshini, kunye nokulungiswa kolwimi lwendalo. I-DSPy yenzelwe ukusebenza ngaphandle komthungo kunye nezikhokelo ezidumileyo ezifana neLangChain kunye ne-FastAPI.

LangChain

I-LangChain yinkqubo yePython yokwakha kunye nokuhambisa izicelo eziqhutywa yi-AI. Ibonelela ngeseti yezixhobo kunye namathala eencwadi okulungisa ulwimi lwendalo, ukufundwa koomatshini, kunye nolwakhiwo lokuqonda. I-LangChain yenzelwe ukusebenza ngokungenamthungo kunye nezikhokelo ezidumileyo ezifana ne-DSPy kunye ne-FastAPI.

Ufezekiso lobuGcisa

Ukubonisa ukuphunyezwa kobugcisa be-orchestrating agent AI kwimibhobho ye-DevOps usebenzisa i-DSPy kunye neLangChain, makhe siqwalasele umzekelo olula. Masithi sifuna ukwenza ngokuzenzekelayo ukuthunyelwa kwesicelo sewebhu usebenzisa umbhobho we-DevOps. Singasebenzisa i-DSPy ukwakha inkqubo yokuthatha izigqibo eqhutywa yi-AI emisela isicwangciso esifanelekileyo sokusasaza ngokusekelwe kwizinto ezifana nokusebenza kwesicelo, impendulo yomsebenzisi, kunye nezixhobo zenkqubo.

ngenisa dSpy
ukusuka kwi-LLM yokungenisa i-langchain


def deployment_strategy(app_performance, user_feedback, system_resources):
    # Sebenzisa i-DSPy ukucubungula idatha kunye nokwenza izigqibo
    idatha = dspy.Data(app_performance, user_feedback, system_resources)
    isigqibo = dspy.DecisionMaker(data)
    
    # Sebenzisa iLangChain ukwenza isicwangciso sokusasaza
    llm = LLM("text-davinci-003")
    isicwangciso = llm.generate_text(f"Beka {app_performance} kunye {user_feedback} kunye {system_resources} izixhobo")
    
    isicwangciso sokubuya

# Chaza umbhobho weDevOps
def umbhobho (app_performance, user_feedback, system_resources):
    # Sebenzisa i-DSPy ukucwangcisa umbhobho
    umbhobho = dspy.Pipeline ()
    pipeline.add_task(deployment_strategy, app_performance, user_feedback, system_resources)
    pipeline.add_task(dspytasks.Deploy, plan)
    
    # Qhuba umbhobho
    pipeline.run()

# Qhuba umbhobho
umbhobho (90, 4.5, 80)

Ephezulu 5-10 Eyeyona Isetyenziswayo Imiyalelo/iziqwengana

Nantsi eyona miyalelo/iziqwengana eziphezulu ezi-5-10 ze-DSPy kunye neLangChain:

1. dspy.Data(): Yenza into yedatha yokusetyenzwa kunye nokwenziwa kwezigqibo.
2. dspy.DecisionMaker(): Wenza izigqibo ngokusekelwe kwidatha kunye nomxholo.
3. LLM.generate_text(): Ivelisa umbhalo ngokusekelwe kwimiyalelo yokufaka.
4. dspytasks.Deploy(): Sebenzisa isicelo esisekwe kwisicwangciso sokusasaza.
5. dspy.Pipeline(): IOrchestrate umbhobho weDevOps.
6. umbhobho.yongeza_umsebenzi(): Yongeza umsebenzi kumbhobho.
7. umbhobho.run(): Iqhuba umbhobho.
8. dspy.Data.process(): Inkqubo yedatha yokwenza izigqibo.
9. dspy.DecisionMaker.decide(): Yenza isigqibo ngokusekelwe kwidatha kunye nomxholo.
10. LLM.generate_text(prompt): Yenza umbhalo ngokusekelwe kwiingcebiso zongeniso.

Umbono

Nanku umfanekiso wombhobho we-AI oqhutywa yi-DevOps usebenzisa iChart.js:

{
  "type": "bar",
  "idatha": {
    "labels": ["ULungiselelo lweData", "Ukwenziwa kwezigqibo", "Ukusasazwa"],
    "iiseti zedatha": [
      {
        "ileyibhile": "Agentic AI",
        "idatha": [10, 20, 30],
        "backgroundColor": "rgba(255, 99, 132, 0.2)",
        "borderColor": "rgba(255, 99, 132, 1)",
        "BorderWidth": 1
      }
    ]
  },
  "iinketho": {
    "izikali": {
      "yAxes": [
        {
          "amanqaku": {
            "beginAtZero": yinyani
          }
        }
      ]
    }
  }
}

Lo mboniso ubonisa izigaba ezithathu zombhobho we-DevOps oqhutywa yi-AI: ukusetyenzwa kwedatha, ukwenza izigqibo, kunye nokusasazwa.

Isiphelo

Kweli nqaku, sihlolisise ingcamango ye-orchestrating agent AI kwiipayipi ze-DevOps usebenzisa i-DSPy kunye neLangChain. Sibonise umzekelo olula wendlela yokwenza ngokuzenzekelayo ukuthunyelwa kwesicelo sewebhu usebenzisa inkqubo yokwenza izigqibo eziqhutywa yi-AI. Sikwabonelele ngombhobho we-AI oqhutywa yi-DevOps usebenzisa iChart.js. Ngokudibanisa i-AI ye-agent kwimibhobho ye-DevOps, imibutho inokufezekisa i-ZeroOps kwaye ifake isofthiwe ngokukhawuleza, ngomgangatho ophezulu, kunye neendleko eziphantsi.