Kuronga AI Agents eDevOps Automation: A LangChain uye DSPy Nzira | B4N1

Kuronga AI Agents eDevOps Automation: A LangChain uye DSPy Nzira

3 months ago · b4n1 Read time: 4 min

Nhanganyaya

Munda weDevOps wakaita shanduko dzakakura mumakore achangopfuura, nekubatanidzwa kweArtificial Intelligence (AI) uye Machine Kudzidza (ML) ichiwedzera mukurumbira. Imwe yenzvimbo dzakakosha idzo AI iri kuita hunhu hwakakura iri munzvimbo ye automation. Nekusimudzira maAI vamiririri, masangano anogona kukwenenzvera kufambiswa kwesoftware, kuderedza zvikanganiso zvemanyorero, uye kugadzirisa hunyanzvi hwese.

Muchinyorwa chino, tichapinda munyika yeAI-inotyairwa DevOps otomatiki, tichitarisa paLangChain uye DSPy masisitimu. Isu tichaongorora kuti maturusi aya angashandiswa sei kugadzira proactive nyaya kugadzirisa masisitimu uye otomatiki software yekuvandudza workflows. Pamusoro pezvo, isu tinopa muenzaniso unoshanda wekubatanidza Makuru Mutauro Models (LLMs) mune yako DevOps pombi.

Chii chinonzi LangChain uye DSPy?

Tisati tanyura muzvinyorwa zveAI-inotyairwa DevOps otomatiki, ngatitarisei muchidimbu kuLangChain uye DSPy masimusi.

LangChain

LangChain inzvimbo yakavhurika-sosi inoita kuti vagadziri vagadzire maapplication eAI ekukurukurirana vachishandisa Large Language Models (LLMs). Inopa iri nyore uye intuitive API yekudyidzana neLLMs, ichibvumira vanogadzira kuti vagadzire tsika dzekukurukurirana nzvimbo dzekushandisa kwavo.

LangChain inotsigira huwandu hwakasiyana hweLLMs, kusanganisira yakakurumbira mhando seLLaMA, BERT, uye RoBERTa. Inopawo huwandu hwezvishandiso zvekunyatso-tuning uye kugadzirisa maLLMs kuti aenderane nemakesi ekushandisa chaiwo.

DSPy

DSPy iraibhurari yePython inopa iri nyore uye inoshanda nzira yekubatanidza AI modhi mune yako DevOps pombi. Inotsigira zvakasiyana-siyana zveAI maitiro, kusanganisira LangChain, uye inopa huwandu hwezvishandiso zve automating AI-driven workflows.

DSPy yakagadzirirwa kushanda isina musono neakakurumbira maturusi eDevOps seJenkins, GitLab, uye Docker, zvichiita kuti ive sarudzo yakanaka kune masangano ari kutsvaga kubatanidza AI mune yavo iripo DevOps zvivakwa.

Kuronga AI Agents eDevOps otomatiki

Zvino zvatava nepfupiso pfupi yeLangChain neDSPy, ngationgororei kuti mafaera aya angashandiswa sei kuronga maAI agents eDevOps otomatiki.

Proactive Issue Resolution

Imwe yemabhenefiti akakosha eAI-inotyairwa DevOps otomatiki iko kugona kugadzirisa nekugadzirisa nyaya dzisati dzave matambudziko makuru. LangChain uye DSPy inogona kushandiswa kugadzira tsika dzekukurukurirana dzinosangana neLLMs kuona zvinogona kuitika uye kupa mazano ekugadzirisa.

Heino muenzaniso wakapfava wekuti izvi zvingashanda sei:

import langchain
kubva langchain.chat_models import LLaMA

# Gadzira LangChain chat modhi
chat_model = LLaMA()

# Tsanangura basa rekudyidzana neiyo chat modhi
def interact_with_chat_model(kukurumidza):
    mhinduro = chat_model.run(prompt)
    dzorera mhinduro

# Tsanangura basa rekuona zvingangoitika
def zivisa_issues(code):
    kukurumidza = f"Ziva zvingangoitika mukodhi inotevera: {code}"
    mhinduro = interact_with_chat_model(kukurumidza)
    dzorera mhinduro

# Tsanangura basa rekupa mazano ekugadzirisa
def provide_recommendations (nyaya):
    kukurumidza = f"Ipa mazano ekugadzirisa zvinotevera: {nyaya}"
    mhinduro = interact_with_chat_model(kukurumidza)
    dzorera mhinduro

# Muenzaniso kushandiswa
kodhi = """
def wedzera(x,y):
    dzoka x +y
"""
nyaya = zivisa_issues(code)
mazano = provide_recommendations (nyaya)
dhinda (recommendations)

Mumuenzaniso uyu, tinotsanangura basa riri nyore interact_with_chat_model rinodyidzana neLangChain chat modhi kuti ipe mhinduro kune yakapihwa kukurumidza. Isu tinozotsanangura maviri ekuwedzera mabasa ziva_matambudziko uye kupa_kurudziro anoshandisa chat modhi kuona zvingangoitika mune imwe sampuli kodhi uye kupa mazano ekugadzirisa.

Automating Software Development Workflows

LangChain uye DSPy inogona zvakare kushandiswa kuita otomatiki software yekuvandudza workflows nekubatanidza AI modhi mune yako iripo DevOps pombi.

Heino muenzaniso wekuti izvi zvingashanda sei:

import dspy
kubva dSpy import Workflow

# Tsanangura DSPy mafambiro ekushanda
workflow = Workflow ()

# Tsanangura basa rekumhanyisa kodhi yekuongorora chishandiso
def run_code_analysis(code):
    # Mhanya kodhi yekuongorora chishandiso kuona zvingangoitika
    nyaya = []
    #...
    return issues

# Tsanangura basa rekupa mazano ekugadzirisa
def provide_recommendations (nyaya):
    # Shandisa iyo LangChain chat modhi kupa mazano
    kukurumidza = f"Ipa mazano ekugadzirisa zvinotevera: {nyaya}"
    mhinduro = langchain.chat_model.run(prompt)
    dzorera mhinduro

# Tsanangura basa rekuendesa iyo kodhi
def deploy_code(code):
    # Tumira kodhi kunzvimbo yekugadzira
    #...

# Tsanangura mafambiro ebasa
workflow.add_task(run_code_analysis)
workflow.add_task(provide_recommendations)
workflow.add_task(deploy_code)

# Chengetedza hurongwa hwekushanda
workflow.run()

Mumuenzaniso uyu, tinotsanangura yakapfava DSPy workflow iyo ine mabasa matatu: kumhanyisa kodhi yekuongorora chishandiso, kupa kurudziro yekugadzirisa, uye kuendesa kodhi. Isu tobva tatsanangura mafambiro ebasa nekuwedzera rimwe nerimwe basa nekurimhanyisa.

Yepamusoro 5-10 Yakanyanya Kushandiswa Mirairo / Zvimedu

Heano mamwe emirairo anonyanya kushandiswa uye zvimedu zveLangChain uye DSPy:

LangChain

  • langchain.chat_models.LLaMA(): Gadzira modhi yekutaura yeLangChain uchishandisa modhi yeLLaMA.
  • langchain.chat_model.run(prompt): Mhanyai modhi yekutaura nekuchimbidza kwakapihwa.
  • langchain.chat_model.interact_with(prompt): Bata nemhando yekutaura uchishandisa kukurumidza kupihwa.

DSPy

  • dspy.Workflow(): Gadzira DSPy workflow.
  • workflow.add_task(basa): Wedzera basa kune mafambiro ebasa.
  • workflow.run(): Mhanya mafambiro ebasa.

Kuona

Heino ratidziro yakapusa yekufamba kwebasa uchishandisa Chart.js:

{
  "type": "bha",
  "data": {
    "labels": ["Code Analysis", "Recommendations", "Deployment"],
    "datasets": [
      {
        "label": "Workflow",
        "data": [10, 20, 30],
        "backgroundColor": [
          "rgba(255, 99, 132, 0.2)",
          "rgba(54, 162, 235, 0.2)",
          "rgba(255, 206, 86, 0.2)"
        ],
        "borderColor": [
          "rgba(255, 99, 132, 1)",
          "rgba(54, 162, 235, 1)",
          "rgba(255, 206, 86, 1)"
        ],
        "borderWidth": 1
      }
    ]
  },
  "sarudzo": {
    "zita": {
      "kuratidza": chokwadi,
      "text": "Basa rekushanda"
    },
    "zvikero": {
      "yAxes": [
        {
          "zvikwekwe": {
            "beginAtZero": chokwadi
          }
        }
      ]
    }
  }
}

Iyi yekuona inoratidza yakapfava bar chart inomiririra mafambiro ebasa, ine mabhawa matatu anomiririra kuongororwa kwekodhi, kurudziro, uye mabasa ekutumira.

Mhedziso

Muchikamu chino, takaongorora nyika yeAI-inotyairwa DevOps otomatiki uchishandisa LangChain uye DSPy. Isu takaona kuti aya masisitimu anogona kushandiswa kugadzira proactive nyaya kugadzirisa masisitimu uye otomatiki software yekuvandudza workflows. Isu takapawo mienzaniso inoshanda yekubatanidza maLLM mupombi yako yeDevOps uye kuona mafambiro ebasa uchishandisa Chart.js.

Nekusimudzira simba reAI uye otomatiki, masangano anogona kukwenenzvera kufambiswa kwesoftware, kuderedza zvikanganiso zvemanyorero, uye kunatsiridza kushanda zvakanaka. Kunyangwe iwe uri injinjini yeDevOps, mugadziri wesoftware, kana mutungamiriri webhizinesi, LangChain neDSPy zvishandiso zvine simba zvinogona kukubatsira kuzadzisa zvinangwa zvako.

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