Ifihan

Ikorita ti Imọye Oríkĕ (AI), faaji sọfitiwia, ati DevOps ti fun ni igbega si apẹrẹ tuntun ni idagbasoke sọfitiwia: AI Agentic. AI Agenttic tọka si lilo adaṣe adaṣe AI lati jẹki ṣiṣe ati imunadoko ti awọn ṣiṣan iṣẹ DevOps. Ninu àpilẹkọ yii, a yoo ṣawari imọran ti Agentic CI / CD ati bi o ṣe le ṣe imuse rẹ nipa lilo DSPy ati LangChain.

Kini Agent CI/CD?

Agentic CI / CD jẹ ṣiṣan iṣẹ DevOps kan ti o ṣe adaṣe adaṣe ti AI-ṣiṣẹ lati ṣe ilana ilana Integration Ilọsiwaju ati Ilọsiwaju Ilọsiwaju. Ọna yii nlo awọn algorithms ikẹkọ ẹrọ lati ṣe itupalẹ data lati awọn orisun oriṣiriṣi, gẹgẹbi awọn ibi ipamọ koodu, kọ awọn olupin, ati awọn irinṣẹ ibojuwo, lati ṣe idanimọ awọn ilana ati awọn aiṣedeede ti o le sọ fun ṣiṣe ipinnu.

Agent CI/CD jẹ apẹrẹ lati mu iṣiṣẹ ati imunadoko ti awọn ṣiṣan iṣẹ DevOps nipasẹ:

** Ṣiṣe awọn iṣẹ-ṣiṣe atunṣe laifọwọyi ***: Aṣeṣe-iwakọ AI le ṣe awọn iṣẹ-ṣiṣe gẹgẹbi awọn atunwo koodu, idanwo, ati imuṣiṣẹ, ti o gba awọn oludasilẹ eniyan laaye lati dojukọ awọn iṣẹ-ṣiṣe ti o ga julọ.
** Ṣiṣayẹwo awọn oran ti o pọju ***: Awọn algorithms ẹkọ ẹrọ le ṣe itupalẹ data lati awọn orisun oriṣiriṣi lati ṣe idanimọ awọn oran ti o pọju ṣaaju ki wọn di awọn iṣoro pataki.
** Ṣiṣapeye awọn iṣan-iṣẹ iṣẹ ***: Agent CI / CD le mu awọn iṣan-iṣẹ ṣiṣẹ nipa idamo awọn ipa-ọna ti o munadoko julọ fun imuṣiṣẹ koodu ati idinku akoko isinmi.

Ṣiṣe Agentic CI/CD pẹlu DSPy ati LangChain

DSPy ati LangChain jẹ awọn ilana olokiki meji fun kikọ awọn eto AI Agent. DSPy jẹ ile-ikawe Python fun kikọ awọn eto pinpin, lakoko ti LangChain jẹ ilana fun kikọ awọn eto AI ibaraẹnisọrọ.

Lati ṣe Agent CI/CD pẹlu DSPy ati LangChain, a yoo lo awọn paati wọnyi:

** DSPy ***: A yoo lo DSPy lati kọ eto ti a pin kaakiri ti o le mu awọn oye nla ti data ati ṣe awọn iṣiro eka.
** LangChain ***: A yoo lo LangChain lati kọ eto AI ibaraẹnisọrọ ti o le ṣe ibaraẹnisọrọ pẹlu awọn olupilẹṣẹ ati pese awọn imọran si ilana CI / CD.

Eyi ni apẹẹrẹ ti o rọrun ti bii o ṣe le lo DSPy ati LangChain lati ṣe imuse Agentic CI/CD: `` Python gbe wọle dspy lati agbewọle LLM langchain

Ṣetumo iṣupọ DSPy kan

iṣupọ = dspy.Cluster()

Ṣetumo LangChain LLM kan

llm = LLM ("ọrọ-davinci-003")

Ṣetumo iṣan-iṣẹ CI / CD kan

def ci_cd_workflow(koodu): # Ṣe itupalẹ koodu nipa lilo DSPy onínọmbà = cluster.analyze(koodu)

# Lo LangChain lati pese awọn oye
imọ = llm.generate_insights(itupalẹ)

# Pada awọn oye pada
pada imọ

Ṣe idanwo iṣan-iṣẹ CI/CD

koodu = "tẹ ('Hello World!')" awọn oye = ci_cd_workflow(koodu) tẹjade (awọn oye)

Koodu yii n ṣalaye ṣiṣan iṣẹ CI / CD ti o nlo DSPy lati ṣe itupalẹ koodu ati LangChain lati pese awọn oye. Iṣẹ `ci_cd_workflow` gba koodu bi titẹ sii, ṣe itupalẹ rẹ nipa lilo DSPy, ati lẹhinna lo LangChain lati ṣe agbekalẹ awọn oye. Awọn oye lẹhinna yoo da pada bi o ṣe jade.

## Top 5-10 Awọn aṣẹ ti a lo julọ / awọn snippets

Eyi ni oke 5-10 ti a lo julọ awọn aṣẹ/snippets fun DSPy ati LangChain:

### DSPy

1. `dspy.Cluster ()`: Ṣẹda a DSPy iṣupọ.
2. `cluster.analyze(koodu)`: Ṣe itupalẹ koodu nipa lilo DSPy.
3. `cluster.deploy(koodu)`: Ran koodu lilo DSPy.
4. `cluster.monitor(koodu)`: Diigi koodu lilo DSPy.
5. `dspy.utils.log(ipele, ifiranṣẹ)`: Ṣe igbasilẹ ifiranṣẹ ni ipele ti a ti sọ tẹlẹ.

### LangChain

1. `LLM ("text-davinci-003")`: Ṣẹda a LangChain LLM.
2. `llm.generate_insights(onínọmbà)`: Ṣe ipilẹṣẹ awọn oye nipa lilo LangChain.
3. `llm.interact(onínọmbà)`: Ibaṣepọ pẹlu awọn olupilẹṣẹ nipa lilo LangChain.
4. `langchain.utils.log(ipele, ifiranṣẹ)`: Ṣe igbasilẹ ifiranṣẹ ni ipele ti a ti sọ tẹlẹ.
5. `langchain.utils.tokenize(ọrọ)`: Tokenize ọrọ nipa lilo LangChain.

## Wiwo

Eyi ni iworan ti iṣan-iṣẹ Agent CI/CD nipa lilo Chart.js:
`` json
{
  "type": "ọgọ",
  "data": {
    "labels": ["Onínọmbà Kóòdù", "Ìran ìjìnlẹ̀", "Ìfilọ́lẹ̀"],
    "awọn ipilẹ data": [
      {
        "label": "DSPy",
        "data": [10, 20, 30],
        "backgroundColor": ["#FF6384", "#36A2EB", "#FFCE56"]
      },
      {
        "label": "LangChain",
        "data": [20, 30, 40],
        "backgroundColor": ["#FF6384", "#36A2EB", "#FFCE56"]
      }
    ]
  },
  "awọn aṣayan": {
    "akọle": {
      "ifihan": otitọ,
      "ọrọ": "Agentic CI/CD Workflow"
    },
    "iwọn": {
      "yAxes": [
        {
          "awọn ami": {
            "beginAtZero": otitọ
          }
        }
      ]
    }
  }
}

Iwoye yii n ṣe afihan iṣan-iṣẹ Agentic CI/CD bi apẹrẹ igi, pẹlu DSPy ati LangChain ti o jẹ aṣoju bi awọn ipilẹ data lọtọ.

Ipari

Ninu àpilẹkọ yii, a ṣawari ero ti Agentic CI / CD ati bi o ṣe le ṣe imuse rẹ nipa lilo DSPy ati LangChain. A jiroro lori awọn anfani ti Agentic CI/CD, pẹlu awọn iṣẹ ṣiṣe atunwi adaṣe, idamo awọn ọran ti o pọju, ati iṣapeye awọn ṣiṣan iṣẹ. A tun pese apẹẹrẹ ti o rọrun ti bi o ṣe le lo DSPy ati LangChain lati ṣe Agentic CI / CD, bakanna bi oke 5-10 ti o lo awọn pipaṣẹ / snippets fun DSPy ati LangChain. Nikẹhin, a pẹlu iworan ti iṣan-iṣẹ Agentic CI/CD nipa lilo Chart.js.

Nipa gbigbe DSPy ati LangChain ṣiṣẹ, awọn olupilẹṣẹ le kọ awọn eto AI Agent ti o mu imunadoko ati imunadoko ti awọn ṣiṣan iṣẹ DevOps ṣiṣẹ. Boya o jẹ olupilẹṣẹ ti igba tabi o kan bẹrẹ, Agentic CI/CD jẹ aaye moriwu ati idagbasoke ni iyara ti o tọ lati ṣawari.

Orchestrating Agentic AI ni DevOps Workflows pẹlu DSPy ati LangChain