Gabatarwa

Juyin juya halin DevOps ya canza yadda ake haɓaka software, turawa, da sarrafa su. Koyaya, yayin da sarƙaƙƙiyar tsarin software na zamani ke haɓaka, haka buƙatar sarrafa kansa, yarda, da daidaitawa. Wannan shine inda DevOps mai amfani da AI ya shigo - tsarin canza wasa wanda ke ba da damar basirar wucin gadi (AI) da koyon injin (ML) don daidaita bututun DevOps. A cikin wannan labarin, za mu bincika yadda ake tsara DevOps masu ƙarfin AI tare da LangChain da DSPy, ƙayyadaddun ƙayyadaddun ƙayyadaddun ƙayyadaddun ƙayyadaddun ƙayyadaddun abubuwa waɗanda ke ba da damar sarrafa tushen wakili.

Menene LangChain?

LangChain wani tsari ne na bude-bude wanda ke ba masu haɓaka damar gina aikace-aikacen AI na tattaunawa ta amfani da tsarin gine-gine mai ƙima. Yana ba da saitin abubuwan da aka riga aka gina da kayan aiki don ƙirƙirar wakilai na AI waɗanda za su iya yin hulɗa tare da tushen bayanai daban-daban, yin ayyuka, da yanke shawara. An gina LangChain a saman harshen shirye-shiryen Rust kuma ana iya amfani da shi tare da wasu harsuna kamar Python da Go.

Menene DSPy?

DSPy ɗakin karatu ne na Python wanda ke ba da sauƙi, ƙirar Pythonic zuwa tsarin LangChain. Yana ba masu haɓakawa damar gina jami'an AI ta amfani da ƙarin sanannun syntax da gudanawar aiki, yana sauƙaƙa haɗa LangChain tare da bututun DevOps na tushen Python.

Haɗa LangChain da DSPy tare da Bututun DevOps

Don haɗa LangChain da DSPy tare da bututun DevOps, muna buƙatar ƙirƙirar wakili na AI wanda zai iya yin ayyuka, yanke shawara, da yin hulɗa tare da maɓuɓɓugar bayanai daban-daban. Anan ga babban matakin bayyani na tsari:

** Ƙayyade Wakilin AI ***: Ƙirƙiri wakili na LangChain wanda zai iya yin ayyuka, kamar ƙaddamar da lambar, gwaje-gwaje masu gudana, da saka idanu.
** Haɗa tare da Kayan aikin DevOps ***: Haɗa wakilin AI tare da kayan aikin DevOps, kamar Jenkins, GitLab CI / CD, ko Docker.
** Sanya Wakilin AI ***: Sanya wakilin AI don yin hulɗa tare da kafofin bayanai daban-daban, kamar bayanan bayanai, APIs, ko tsarin fayil.
** Horar da Wakilin AI ***: Horar da wakilin AI ta amfani da algorithms na koyon injin da bayanai daga bututun DevOps.

Misalin Aiki: Ƙaddamar da Code tare da LangChain da DSPy

Ga misali mai sauƙi na yadda ake tura lamba ta amfani da LangChain da DSPy: `` Python shigo da langchain daga langchain.Agents shigo da Agent daga langchain.llms shigo da LLM

Ƙirƙiri wakilin LangChain

wakili = wakili ( lm=LLM( model_name = "davinci", max_tokens=2048, zafin jiki = 0.5, ), sarkar=[ langchain. Sarkar ( suna = "deploy_code", matakai=[ langchain. Mataki( suna = "git_clone", func=lambda: git_clone("https://github.com/user/repo.git"), ), langchain. Mataki( suna = "build_code", func=lambda: build_code(), ), langchain. Mataki( suna = "deploy_code", func=lambda: deploy_code(), ), ], ), ], )

Sanya lambar ta amfani da wakilin AI

agent.deploy_code()

A cikin wannan misalin, mun ƙirƙiri wakilin LangChain wanda ke yin matakai uku:

1. Yana rufe wurin ajiyar lambar ta amfani da Git.
2. Gina lambar ta amfani da kayan aikin gini.
3. Ƙaddamar da lambar ta amfani da kayan aikin ƙaddamarwa.

Wakilin AI yana amfani da samfurin harshe (LLM) don yanke shawara da aiwatar da ayyuka.

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

Anan akwai wasu umarni da snippets da aka fi amfani da su don LangChain da DSPy:

1. ** Ƙirƙirar wakili na LangChain **: `wakili = Wakili (llm = LLM (samfurin_name = "davinci", max_tokens = 2048, zafin jiki = 0.5), sarkar = [langchain.Chain (sunan = "deploy_code", matakai = [langchain. Mataki (sunan = "git_clone", da: func). git_clone("https://github.com/user/repo.git"))]))
2. ** Sanya lambar ta amfani da wakilin AI **: `agent.deploy_code()`
3. ** Horar da wakilin AI **: `agent.train (bayanai = [{"shigarwar": "deploy code", "fitarwa": "lambar da aka tura"}])'
4. ** Haɗa tare da kayan aikin DevOps**: `agent.integrate_with_jenkins()`
5. ** Sanya wakilin AI **: `agent.configure (llm = LLM (samfurin_name = "davinci", max_tokens=2048, zafin jiki = 0.5))`
6. ** Sami matsayin wakilin AI **: `agent.status()`
7. ** Sami rajistan ayyukan wakilin AI **: `agent.logs()`
8. ** Sabunta saitunan AI na wakili **: `agent.update_config(llm=LLM (model_name = "davinci", max_tokens=2048, zazzabi=0.5))`
9. ** Share wakilin AI **: `agent.delete()`
10. ** Sami ma'auni na wakilin AI ***: `agent.metrics()`

## Kallon gani

Anan ga hangen nesa na LangChain da gine-ginen DSPy ta amfani da Chart.js:
Json
{
  "type": "bar",
  "data": {
    "labels": ["LangChain", "DSPy"],
    "datasets": [
      {
        "label": "Components",
        "bayanai": [10, 5],
        "Bayan launi": ["#4CAF50", "#FF9800"],
        "BorderColor": ["#3e8e41", "#ff9900"],
        "BorderWidth": 1
      }
    ]
  },
  "zaɓi": {
    "take": {
      "nuna": gaskiya,
      "rubutu": "LangChain da DSPy Architecture"
    },
    "tatsuniya": {
      "nuni": gaskiya
    },
    "ma'auni": {
      "yAxes": [
        {
          "zama": {
            "beginAtZero": gaskiya
          }
        }
      ]
    }
  }
}

Wannan hangen nesa yana nuna adadin abubuwan haɗin gwiwa a cikin LangChain da DSPy.

Kammalawa

A cikin wannan labarin, mun bincika yadda ake tsara DevOps masu ƙarfin AI tare da LangChain da DSPy, ƙayyadaddun ƙayyadaddun ƙirar ƙira guda biyu waɗanda ke ba da damar sarrafa tushen wakili. Mun tattauna fa'idodin DevOps masu ƙarfi na AI, gami da haɓaka aiki da kai, yarda, da haɓakawa. Mun kuma ba da misali mai amfani na tura lamba ta amfani da LangChain da DSPy, da kuma jerin umarni da snippets da aka fi amfani da su. A ƙarshe, mun hango LangChain da gine-ginen DSPy ta amfani da Chart.js. Tare da LangChain da DSPy, masu haɓakawa za su iya gina ingantattun bututun DevOps masu inganci, masu daidaitawa, da sarrafa kansu waɗanda ke ba da ƙarfin AI da koyan injin.

Haɓaka DevOps masu ƙarfi na AI tare da LangChain da DSPy: Jagorar Fasaha zuwa Kayan Aiki na tushen Wakili