** Orchestrating Agentic AI a cikin DevOps Pipelines tare da DSPy da LangChain don Ingantaccen ZeroOps ***

** Gabatarwa ***

Haɗin kai na Artificial Intelligence (AI) a cikin bututun DevOps ya canza yadda ake haɓaka software, gwadawa, da turawa. AI Agent, wani yanki na AI wanda ke ba da damar yanke shawara mai cin gashin kansa da aiwatar da ayyuka, yana da yuwuwar ƙara haɓaka inganci da tasirin ayyukan DevOps. A cikin wannan labarin, za mu bincika manufar ƙungiyar AI a cikin bututun DevOps ta amfani da DSPy da LangChain, kuma za mu tattauna fa'idodin wannan hanyar don cimma ZeroOps.

** Menene Agent AI?**

Agent AI yana nufin nau'in AI wanda ke da ikon yanke shawara da ɗaukar ayyuka da kansa, ba tare da sa hannun ɗan adam ba. Ana samun wannan ta hanyar amfani da na'urori na zamani na koyan na'ura, sarrafa harshe na halitta, da kuma gine-ginen fahimi. Tsarin AI na asali na iya koyo daga gogewa, daidaitawa zuwa sabbin yanayi, da yin hulɗa tare da mutane ta hanyar da ta fi dacewa da hankali.

Menene ZeroOps?

ZeroOps falsafar DevOps ce da ke nufin kawar da buƙatar sa hannun ɗan adam a cikin haɓaka software, turawa, da kiyayewa. Ana samun wannan ta hanyar amfani da aiki da kai, ƙungiyar kade-kade, da yanke shawara ta hanyar AI. ZeroOps yana bawa ƙungiyoyi damar tura software cikin sauri, tare da inganci mafi girma, kuma a ƙananan farashi.

** Orchestrating Agent AI a cikin Bututun DevOps ***

Don tsara AI mai aiki a cikin bututun DevOps, muna buƙatar haɗa shawarar yanke shawara ta AI da ɗaukar aiki a cikin bututun. Ana iya samun wannan ta hanyar amfani da DSPy da LangChain, mashahuran ginshiƙai guda biyu don ginawa da ƙaddamar da aikace-aikacen AI.

DSPy

DSPy tsarin Python ne don ginawa da tura aikace-aikacen AI. Yana ba da saitin kayan aiki da ɗakunan karatu don sarrafa bayanai, koyon injin, da sarrafa harshe na halitta. An ƙera DSPy don yin aiki ba tare da matsala ba tare da mashahurin tsarin kamar LangChain da FastAPI.

LangChain

LangChain tsari ne na Python don ginawa da tura aikace-aikacen AI. Yana ba da saitin kayan aiki da ɗakunan karatu don sarrafa harshe na halitta, koyan inji, da kuma gine-ginen fahimi. An ƙera LangChain don yin aiki ba tare da matsala ba tare da mashahuran tsarin aiki kamar DSPy da FastAPI.

Aikin Fasaha

Don nuna aiwatar da fasaha na ƙungiyar AI a cikin bututun DevOps ta amfani da DSPy da LangChain, bari mu ɗauki misali mai sauƙi. A ce muna son sarrafa aikin tura aikace-aikacen yanar gizo ta amfani da bututun DevOps. Za mu iya amfani da DSPy don gina tsarin yanke shawara na AI wanda ke ƙayyade mafi kyawun dabarun turawa bisa dalilai kamar aikin aikace-aikacen, ra'ayoyin mai amfani, da albarkatun tsarin.

`` Python shigo da dspy daga langchain shigo da LLM

def deployment_strategy(app_performance, user_feedback, system_resources): # Yi amfani da DSPy don aiwatar da bayanai da yanke shawara data = dspy.Data(app_performance, mai amfani_feedback, system_resources) yanke shawara = dspy.DecisionMaker(bayanai)

# Yi amfani da LangChain don samar da shirin turawa
llm = LLM ("rubutu-davinci-003")
plan = llm.generate_text (f"Aika {app_performance} tare da {user_feedback} da {system_resources} albarkatun))

dawo da shirin

Ƙayyade bututun DevOps

def bututun (app_performance, mai amfani_feedback, system_resources): # Yi amfani da DSPy don tsara bututun bututu = dspy.Pipeline() pipeline.add_task (deployment_strategy, app_performance, user_feedback, system_resources) pipeline.add_task(dspytasks.Deploy, plan)

# Guda bututun
pipeline.run()

Guda bututun

bututu (90, 4.5, 80)

** Manyan Dokoki 5-10 da Akafi Amfani da su/Snippets**

Anan akwai manyan 5-10 da aka fi amfani da umarni/snippets don DSPy da LangChain:

1. `dspy.Data()`: Yana ƙirƙira wani abu na bayanai don sarrafawa da yanke shawara.
2. `dspy.DecisionMaker()`: Yana yanke shawara dangane da bayanai da mahallin.
3. `LLM.generate_text()`: Yana haifar da rubutu dangane da shigar da bayanai.
4. `dspytasks.Deploy()`: Yana tura aikace-aikace bisa tsarin turawa.
5. `dspy.Pipeline()`: Yana tsara bututun DevOps.
6. `bututun.add_task()`: Yana ƙara ɗawainiya zuwa bututun.
7. `bututun.run()`: Guda bututun.
8. `dspy.Data.process()`: Yana aiwatar da bayanai don yanke shawara.
9. `dspy.DecisionMaker.decide()`: Yana yanke shawara dangane da bayanai da mahallin.
10. `LLM.generate_text(prompt)`: Yana haifar da rubutu dangane da shigar da bayanai.

**Kallon gani**

Anan ga hangen nesa na bututun DevOps mai sarrafa AI ta amfani da Chart.js:
Json
{
  "type": "bar",
  "data": {
    "labels": ["Tsarin Bayanai", "Yin yanke shawara", "Tsarin aiki"],
    "datasets": [
      {
        "label": "Agent AI",
        "bayanai": [10, 20, 30],
        "backgroundColor": "rgba(255, 99, 132, 0.2)",
        "BorderColor": "rgba(255, 99, 132, 1)",
        "BorderWidth": 1
      }
    ]
  },
  "zaɓi": {
    "ma'auni": {
      "yAxes": [
        {
          "zama": {
            "beginAtZero": gaskiya
          }
        }
      ]
    }
  }
}

Wannan hangen nesa yana nuna matakai uku na bututun DevOps na AI da ke jagorantar: sarrafa bayanai, yanke shawara, da turawa.

Kammalawa

A cikin wannan labarin, mun bincika manufar ƙungiyar AI a cikin bututun DevOps ta amfani da DSPy da LangChain. Mun nuna misali mai sauƙi na yadda ake sarrafa aikin aika aikace-aikacen yanar gizo ta amfani da tsarin yanke shawara na AI. Mun kuma ba da hangen nesa na bututun DevOps mai sarrafa AI ta amfani da Chart.js. Ta hanyar haɗa AI mai aiki a cikin bututun DevOps, ƙungiyoyi za su iya cimma ZeroOps da tura software cikin sauri, tare da inganci mafi girma, kuma cikin ƙananan farashi.