Orchestrating Agetic AI mu DevOps Pipelines ndi DSPy ndi LangChain for Enhanced ZeroOps

Kupanga Agentic AI mu Mapaipi a DevOps okhala ndi DSPy ndi LangChain kwa Enhanced ZeroOps

Chiyambi

Kuphatikiza kwa Artificial Intelligence (AI) mu mapaipi a DevOps kwasintha momwe mapulogalamu amapangidwira, kuyesedwa, ndi kutumizidwa. Agentik AI, gulu laling'ono la AI lomwe limathandizira kupanga zisankho modziyimira pawokha komanso kuchitapo kanthu, lili ndi kuthekera kopititsa patsogolo magwiridwe antchito a DevOps. M'nkhaniyi, tiwona lingaliro la orchestrating agent AI mu DevOps mapaipi pogwiritsa ntchito DSPy ndi LangChain, ndikukambirana za ubwino wa njirayi pokwaniritsa ZeroOps.

Agent AI ndi chiyani?

Agentic AI imatanthawuza mtundu wa AI womwe uli ndi kuthekera kopanga zisankho ndikuchitapo kanthu modziyimira pawokha, popanda kulowererapo kwa anthu. Izi zimatheka pogwiritsa ntchito makina apamwamba ophunzirira makina, kukonza zilankhulo zachilengedwe, komanso kamangidwe kanzeru. Machitidwe a Agetic AI amatha kuphunzira kuchokera pazomwe akumana nazo, kuzolowera zochitika zatsopano, ndikuyanjana ndi anthu mwachilengedwe komanso mwachilengedwe.

ZeroOps ndi chiyani?

ZeroOps ndi filosofi ya DevOps yomwe ikufuna kuthetsa kufunikira kwa kulowererapo kwa anthu pakupanga mapulogalamu, kutumiza, ndi kukonza. Izi zimatheka pogwiritsa ntchito ma automation, orchestration, ndi kupanga zisankho koyendetsedwa ndi AI. ZeroOps imathandizira mabungwe kutumiza mapulogalamu mwachangu, ndipamwamba kwambiri, komanso pamitengo yotsika.

**Kupanga Agentic AI mu Mapaipi a DevOps **

Kuti tikonzekere AI yodalirika mu mapaipi a DevOps, tifunika kuphatikiza kupanga zisankho motsogozedwa ndi AI ndikuchitapo kanthu. Izi zitha kutheka pogwiritsa ntchito DSPy ndi LangChain, magawo awiri otchuka pomanga ndi kutumiza mapulogalamu oyendetsedwa ndi AI.

DSPy

DSPy ndi dongosolo la Python lomanga ndi kutumiza mapulogalamu oyendetsedwa ndi AI. Amapereka zida ndi malaibulale opangira ma data, kuphunzira pamakina, ndikusintha zilankhulo zachilengedwe. DSPy idapangidwa kuti izigwira ntchito mosasunthika ndi machitidwe otchuka monga LangChain ndi FastAPI.

LangChain

LangChain ndi chimango cha Python chomanga ndi kutumiza ntchito zoyendetsedwa ndi AI. Imakhala ndi zida ndi malaibulale osinthira zilankhulo zachilengedwe, kuphunzira pamakina, ndi zomangamanga zanzeru. LangChain idapangidwa kuti izigwira ntchito mosasunthika ndi zida zodziwika bwino monga DSPy ndi FastAPI.

Kukhazikitsa kwaukadaulo

Kuwonetsa kukhazikitsidwa kwaukadaulo kwa orchestrating agent AI mu mapaipi a DevOps pogwiritsa ntchito DSPy ndi LangChain, tiyeni tilingalire chitsanzo chosavuta. Tiyerekeze kuti tikufuna kupanga makina ogwiritsira ntchito intaneti pogwiritsa ntchito payipi ya DevOps. Titha kugwiritsa ntchito DSPy kupanga njira yopangira zisankho yoyendetsedwa ndi AI yomwe imasankha njira yabwino yotumizira zinthu kutengera zinthu monga magwiridwe antchito, mayankho a ogwiritsa ntchito, ndi zida zamakina.

kuitanitsa dSpy
kuchokera ku langchain import LLM


def deployment_strategy(app_performance, user_feedback, system_resources):
    # Gwiritsani ntchito DSPy kukonza deta ndikupanga zisankho
    data = dspy.Data(app_performance, user_feedback, system_resources)
    chisankho = dSpy.DecisionMaker(data)
    
    # Gwiritsani ntchito LangChain kupanga mapulani otumizira
    llm = LLM("text-davinci-003")
    plan = llm.generate_text(f"Deploy {app_performance} ndi {user_feedback} ndi {system_resources} zothandizira")
    
    ndondomeko yobwerera

# Tanthauzirani payipi ya DevOps
def pipeline (app_performance, user_feedback, system_resources):
    # Gwiritsani ntchito DSPy kukonza mapaipi
    pipeline = dSpy.Pipeline()
    pipeline.add_task(deployment_strategy, app_performance, user_feedback, system_resources)
    pipeline.add_task(dspytasks.Deploy, plan)
    
    # Yambitsani bomba
    pipeline.run()

# Yambitsani bomba
mapaipi (90, 4.5, 80)

**Malamulo/Zidutswa Zapamwamba za 5-10 **

Nawa malamulo / zidule za 5-10 zomwe zimagwiritsidwa ntchito kwambiri pa DSPy ndi LangChain:

dspy.Data(): Imapanga chinthu cha data kuti chikonzedwe ndi kupanga zisankho.
dspy.DecisionMaker(): Imapanga zisankho motengera deta ndi nkhani.
LLM.generate_text(): Amapanga mawu potengera zomwe amalowetsa.
dspytasks.Deploy(): Imatumiza ntchito kutengera dongosolo la kutumiza.
dspy.Pipeline(): Amayimba mapaipi a DevOps.
pipeline.add_task(): Iwonjezera ntchito paipi.
pipeline.run(): Imayendetsa payipi.
dspy.Data.process(): Imakonza deta popanga zisankho.
dspy.DecisionMaker.decide(): Amapanga chisankho potengera deta ndi nkhani.
LLM.generate_text(prompt): Amapanga mawu potengera zomwe amalowetsa.

**Kuwona **

Nayi chithunzithunzi cha mapaipi a DevOps oyendetsedwa ndi AI pogwiritsa ntchito Chart.js:

{
  "type": "bar",
  "data": {
    "labels": ["Kukonza Data", "Kupanga zisankho", "Deployment"],
    "ma dataset": [
      {
        "label": "Agent AI",
        "data": [10, 20, 30],
        "backgroundColor": "rgba(255, 99, 132, 0.2)",
        "borderColor": "rgba(255, 99, 132, 1)",
        "BorderWidth": 1
      }
    ]
  },
  "zosankha": {
    "masikelo": {
      "yAxes": [
        {
          "nkhupakupa": {
            "beginAtZero": zoona
          }
        }
      ]
    }
  }
}

Kuwoneka uku kukuwonetsa magawo atatu a payipi ya AI yoyendetsedwa ndi AI ya DevOps: kukonza deta, kupanga zisankho, ndi kutumiza.

Mapeto

M'nkhaniyi, tafufuza lingaliro la orchestrating agent AI mu mapaipi a DevOps pogwiritsa ntchito DSPy ndi LangChain. Tidawonetsa chitsanzo chosavuta chamomwe mungapangire makina ogwiritsira ntchito intaneti pogwiritsa ntchito njira yopangira zisankho yoyendetsedwa ndi AI. Tidaperekanso chithunzithunzi cha mapaipi a DevOps oyendetsedwa ndi AI pogwiritsa ntchito Chart.js. Mwa kuphatikiza ma AI agent mu mapaipi a DevOps, mabungwe amatha kukwaniritsa ZeroOps ndikutumiza mapulogalamu mwachangu, ndipamwamba kwambiri, komanso pamitengo yotsika.

Orchestrating Agetic AI mu DevOps Pipelines ndi DSPy ndi LangChain for Enhanced ZeroOps

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