I-Orchestrating Agent AI kumapayipi e-DevOps ane-DSPy ne-LangChain ukuze uthole ama-ZeroOps athuthukisiwe

Ihlela i-Agentic AI kumapayipi e-DevOps ane-DSPy ne-LangChain ye-Ehanced ZeroOps

Isingeniso

Ukuhlanganiswa kwe-Artificial Intelligence (AI) kumaphayiphi we-DevOps kuguqule indlela isofthiwe ethuthukiswa ngayo, evivinywa ngayo, nesetshenziswa ngayo. I-Agentic AI, isethi engaphansi ye-AI eyenza izinqumo ezizimele kanye nokuthatha izinyathelo, inamandla okuqhubeka nokuthuthukisa ukusebenza kahle nokusebenza kwezinqubo ze-DevOps. Kulesi sihloko, sizohlola umqondo wokuhlela i-AI ye-agent kumapayipi we-DevOps sisebenzisa i-DSPy ne-LangChain, futhi sixoxe ngezinzuzo zale ndlela ekuzuzeni i-ZeroOps.

Iyini i-Agent AI?

I-Agent AI ibhekisela ohlotsheni lwe-AI olunamandla okwenza izinqumo kanye nokwenza izenzo ngokuzenzakalelayo, ngaphandle kokungenelela komuntu. Lokhu kufinyelelwa ngokusebenzisa ama-algorithms okufunda komshini athuthukile, ukucutshungulwa kolimi lwemvelo, nezakhiwo zokuqonda. Amasistimu e-AI e-Agent angafunda kokuhlangenwe nakho, azivumelanise nezimo ezintsha, futhi ahlanganyele nabantu ngendlela engokwemvelo nenembile.

Yini i-ZeroOps?

I-ZeroOps ifilosofi ye-DevOps ehlose ukuqeda isidingo sokungenelela komuntu ekuthuthukisweni kwesofthiwe, ukuthunyelwa, nokugcinwa kwayo. Lokhu kufezwa ngokusebenzisa i-automation, orchestration, kanye nokwenziwa kwezinqumo okuqhutshwa yi-AI. I-ZeroOps inika amandla izinhlangano ukuthi zikhiphe isoftware ngokushesha, ngekhwalithi ephezulu, nangezindleko eziphansi.

I-Orchestrating Agent AI kumapayipi e-DevOps

Ukuze sihlanganise i-AI ye-agent kumapayipi e-DevOps, sidinga ukuhlanganisa ukwenza izinqumo okuqhutshwa yi-AI kanye nokuthatha izinyathelo emgqeni. Lokhu kungafezwa ngokusebenzisa i-DSPy ne-LangChain, izinhlaka ezimbili ezidumile zokwakha kanye nokuthumela izinhlelo zokusebenza eziqhutshwa yi-AI.

DSPy

I-DSPy iwuhlaka lwePython lokwakha nokuthumela izinhlelo zokusebenza eziqhutshwa yi-AI. Ihlinzeka ngesethi yamathuluzi namalabhulali okucubungula idatha, ukufunda ngomshini, nokucubungula ulimi lwemvelo. I-DSPy yakhelwe ukusebenza ngaphandle komthungo nezinhlaka ezidumile ezifana ne-LangChain ne-FastAPI.

LangChain

I-LangChain iwuhlaka lwePython lokwakha nokuthumela izinhlelo zokusebenza eziqhutshwa yi-AI. Ihlinzeka ngesethi yamathuluzi nemitapo yolwazi yokucutshungulwa kolimi lwemvelo, ukufunda ngomshini, nezakhiwo zokuqonda. I-LangChain yakhelwe ukusebenza ngaphandle komthungo nezinhlaka ezidumile ezifana ne-DSPy ne-FastAPI.

Ukusetshenziswa Kwezobuchwepheshe

Ukubonisa ukusetshenziswa kobuchwepheshe kokuhlela i-AI ye-agent kumapayipi e-DevOps kusetshenziswa i-DSPy ne-LangChain, ake sicabangele isibonelo esilula. Ake sithi sifuna ukwenza ngokuzenzakalelayo ukuthunyelwa kohlelo lokusebenza lwewebhu sisebenzisa ipayipi le-DevOps. Singasebenzisa i-DSPy ukuze sakhe isistimu yokuthatha izinqumo eshayelwa yi-AI enquma isu elilungile lokuphakela ngokusekelwe kuzinto ezifana nokusebenza kohlelo lokusebenza, impendulo yomsebenzisi, nezinsiza zesistimu.

ngenisa i-dspy
kusuka ku-langchain yokungenisa i-LLM


def deployment_strategy(app_performance, user_feedback, system_resources):
    # Sebenzisa i-DSPy ukucubungula idatha nokwenza izinqumo
    idatha = dspy.Data(app_performance, user_feedback, system_resources)
    isinqumo = dspy.DecisionMaker(idatha)
    
    # Sebenzisa i-LangChain ukwenza uhlelo lokuthunyelwa
    llm = LLM("text-davinci-003")
    plan = llm.generate_text(f"Faka {app_performance} nge-{user_feedback} kanye nezinsiza {system_resources}")
    
    uhlelo lokubuyisela

# Chaza ipayipi le-DevOps
def pipeline(app_performance, user_feedback, system_resources):
    # Sebenzisa i-DSPy ukuhlela ipayipi
    iphayiphi = dspy.Pipeline()
    pipeline.add_task(deployment_strategy, app_performance, user_feedback, system_resources)
    pipeline.add_task(dspytasks.Deploy, plan)
    
    # Qalisa ipayipi
    pipeline.run()

# Qalisa ipayipi
ipayipi(90, 4.5, 80)

Imiyalo/Amazwibela Aphezulu Okuyi-5-10

Nayi imiyalo/ amazwibela asetshenziswa kakhulu angu-5-10 e-DSPy ne-LangChain:

1. dspy.Data(): Idala into yedatha ukuze icutshungulwe futhi yenze izinqumo.
2. dspy.DecisionMaker(): Yenza izinqumo ngokusekelwe kudatha nomongo.
3. LLM.generate_text(): Ikhiqiza umbhalo ngokusekelwe ekwazisweni okokufaka.
4. dspytasks.Deploy(): Isebenzisa uhlelo lokusebenza ngokusekelwe kuhlelo lokuphakelwa.
5. dspy.Pipeline(): Ihlela ipayipi le-DevOps.
6. ipayipi.add_task(): Yengeza umsebenzi epayipini.
7. ipayipi.run(): Isebenzisa ipayipi.
8. dspy.Data.process(): Icubungula idatha ukuze kuthathwe izinqumo.
9. dspy.DecisionMaker.decide(): Yenza isinqumo ngokusekelwe kudatha nomongo.
10. LLM.generate_text(prompt): Ikhiqiza umbhalo ngokusekelwe ekwazisweni okokufaka.

Ukubona

Nakhu ukubuka ngeso kwepayipi le-DevOps eliqhutshwa yi-AI lisebenzisa i-Chart.js:

{
  "type": "bar",
  "idatha": {
    "labels": ["Ukucubungula Idatha", "Ukwenza Izinqumo", "Ukuthunyelwa"],
    "idathasethi": [
      {
        "label": "Agent AI",
        "idatha": [10, 20, 30],
        "backgroundColor": "rgba(255, 99, 132, 0.2)",
        "borderColor": "rgba(255, 99, 132, 1)",
        "BorderWidth": 1
      }
    ]
  },
  "izinketho": {
    "izikali": {
      "yAxes": [
        {
          "imikhaza": {
            "beginAtZero": kuyiqiniso
          }
        }
      ]
    }
  }
}

Lokhu kubukwa kukhombisa izigaba ezintathu zepayipi le-DevOps eliqhutshwa yi-AI: ukucubungula idatha, ukwenza izinqumo, kanye nokusatshalaliswa.

Isiphetho

Kulesi sihloko, sihlole umqondo wokwenza i-AI ye-agent kumapayipi e-DevOps kusetshenziswa i-DSPy ne-LangChain. Sibonise isibonelo esilula sendlela yokwenza ngokuzenzakalelayo ukuthunyelwa kwesicelo sewebhu kusetshenziswa uhlelo lokuthatha izinqumo oluqhutshwa yi-AI. Siphinde sahlinzeka ngokubonwa kwepayipi le-DevOps eliqhutshwa yi-AI lisebenzisa i-Chart.js. Ngokuhlanganisa i-AI ye-agent kumapayipi we-DevOps, izinhlangano zingafinyelela ku-ZeroOps futhi zikhiphe isofthiwe ngokushesha, ngekhwalithi ephezulu, futhi ngezindleko eziphansi.