Gabatarwa

Haɗin kai na Artificial Intelligence (AI), ƙirar software, da DevOps sun haifar da sabon salo a cikin haɓaka software: Agent AI. Agent AI yana nufin amfani da AI-kore aiki da kai don haɓaka inganci da ingancin ayyukan ayyukan DevOps. A cikin wannan labarin, za mu bincika manufar Agentic CI / CD da kuma yadda ake aiwatar da shi ta amfani da DSPy da LangChain.

Menene Agent CI/CD?

Agent CI / CD shine aikin DevOps wanda ke ba da damar yin aiki da kai da AI don daidaita tsarin Haɗin Ci gaba da Ci gaba da Aiwatar da Aiki. Wannan hanya tana amfani da algorithms koyan na'ura don nazarin bayanai daga maɓuɓɓuka daban-daban, kamar wuraren ajiyar lambobin, gina sabobin, da kayan aikin sa ido, don gano alamu da abubuwan da ba su da kyau waɗanda za su iya sanar da yanke shawara.

An ƙera Agent CI/CD don haɓaka inganci da ingancin ayyukan ayyukan DevOps ta:

** Aiwatar da ayyuka masu maimaitawa ***: Aiwatar da AI na iya yin ayyuka kamar sake dubawa na lamba, gwaji, da turawa, yantar da masu haɓaka ɗan adam don mai da hankali kan ayyuka masu girma.
**Gano abubuwan da zasu iya faruwa ***: Algorithms na koyon injin na iya yin nazarin bayanai daga mabambanta daban-daban don gano abubuwan da za su iya yiwuwa kafin su zama manyan matsaloli.
** Inganta ayyukan aiki ***: Agent CI / CD na iya haɓaka ayyukan aiki ta hanyar gano hanyoyin da suka fi dacewa don ƙaddamar da lambar da rage raguwa.

Aiwatar da Agent CI/CD tare da DSPy da LangChain

DSPy da LangChain mashahuran tsare-tsare biyu ne don gina tsarin AI Agent. DSPy shine ɗakin karatu na Python don gina tsarin rarraba, yayin da LangChain shine tsarin gina tsarin AI na tattaunawa.

Don aiwatar da Agent CI/CD tare da DSPy da LangChain, za mu yi amfani da abubuwan da ke biyowa:

** DSPy ***: Za mu yi amfani da DSPy don gina tsarin da aka rarraba wanda zai iya ɗaukar bayanai masu yawa da kuma yin ƙididdiga masu rikitarwa.
** LangChain ***: Za mu yi amfani da LangChain don gina tsarin AI na tattaunawa wanda zai iya hulɗa tare da masu haɓakawa da kuma ba da haske game da tsarin CI / CD.

Ga misali mai sauƙi na yadda ake amfani da DSPy da LangChain don aiwatar da Agentic CI/CD: `` Python shigo da dspy daga langchain shigo da LLM

Ƙayyade gungu na DSPy

tari = dspy.Cluster()

ayyana LangChain LLM

llm = LLM ("rubutu-davinci-003")

Ƙayyade aikin CI / CD

def ci_cd_workflow(lambar): # Yi nazarin lambar ta amfani da DSPy analysis = cluster.analyze(lambar)

# Yi amfani da LangChain don ba da haske
fahimta = lm.generate_insights(bincike)

# Koma bayanan
dawo da fahimta

Gwada aikin CI/CD

code = "bugu ('Hello Duniya!')" fahimta = ci_cd_workflow(lambar) bugu (hankali)

Wannan lambar ta bayyana aikin CI / CD wanda ke amfani da DSPy don nazarin lamba da LangChain don samar da fahimta. Aikin 'ci_cd_workflow' yana ɗaukar lamba azaman shigarwa, yana nazarin shi ta amfani da DSPy, sannan yana amfani da LangChain don samar da fahimta. Ana mayar da bayanan a matsayin fitarwa.

## 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:

### DSPy

1. `dspy.Cluster()`: Yana ƙirƙira gungu na DSPy.
2. `cluster.analyze(code)`: Yana nazarin lamba ta amfani da DSPy.
3. `cluster.deploy(code)`: Yana tura lambar ta amfani da DSPy.
4. `cluster.monitor(code)`: Yana lura da lambar ta amfani da DSPy.
5. `dspy.utils.log(matakin, saƙo)`: Shigar da saƙo a ƙayyadadden matakin.

### LangChain

1. `LLM("rubutu-davinci-003")': Yana Ƙirƙirar LangChain LLM.
2. `lm.generate_insights(bincike)`: Yana haifar da fahimta ta amfani da LangChain.
3. `lm.interact(bincike)`: Yana hulɗa tare da masu haɓakawa ta amfani da LangChain.
4. `langchain.utils.log(mataki, saƙo)`: Yana shigar da saƙo a ƙayyadadden matakin.
5. `langchain.utils.tokenize(rubutu)`: Alamar rubutu ta amfani da LangChain.

## Kallon gani

Anan ga hangen nesa na Agent CI/CD gudanawar aiki ta amfani da Chart.js:
Json
{
  "type": "bar",
  "data": {
    "labels": ["Label Analysis", "Insight Generation", "Tsarin aiki"],
    "datasets": [
      {
        "label": "DSPy",
        "bayanai": [10, 20, 30],
        "Bayan launi": ["#FF6384", "#36A2EB", "#FFCE56"]
      },
      {
        "label": "LangChain",
        "bayanai": [20, 30, 40],
        "Bayan launi": ["#FF6384", "#36A2EB", "#FFCE56"]
      }
    ]
  },
  "zaɓi": {
    "take": {
      "nuna": gaskiya,
      "rubutu": "Agent CI/CD Gudun Aiki"
    },
    "ma'auni": {
      "yAxes": [
        {
          "zama": {
            "beginAtZero": gaskiya
          }
        }
      ]
    }
  }
}

Wannan hangen nesa yana nuna aikin Agent CI/CD azaman ginshiƙi, tare da DSPy da LangChain waɗanda aka wakilta azaman saitin bayanai daban.

Kammalawa

A cikin wannan labarin, mun bincika manufar Agentic CI / CD da kuma yadda ake aiwatar da shi ta amfani da DSPy da LangChain. Mun tattauna fa'idodin Agentic CI/CD, gami da ayyuka masu maimaitawa na atomatik, gano abubuwan da za su yuwu, da haɓaka ayyukan aiki. Mun kuma ba da misali mai sauƙi na yadda ake amfani da DSPy da LangChain don aiwatar da Agentic CI / CD, da kuma manyan 5-10 mafi yawan amfani da umarni / snippets don DSPy da LangChain. A ƙarshe, mun haɗa da hangen nesa na Agent CI/CD aikin gudana ta amfani da Chart.js.

Ta hanyar yin amfani da DSPy da LangChain, masu haɓakawa za su iya gina tsarin AI na Agent wanda ke haɓaka inganci da tasiri na ayyukan aiki na DevOps. Ko kai ƙwararren ƙwararren mai haɓaka ne ko kuma farawa, Agent CI/CD filin ne mai ban sha'awa da haɓaka cikin sauri wanda ya cancanci bincike.

Gudanar da Agent AI a cikin DevOps Workflows tare da DSPy da LangChain