** Buɗe ZeroOps tare da Agents AI da Injiniyan Mahimmanci: Tsarin Tsarin Tsarin Ga AI DevOps Automation ta amfani da StackGen da LangChain ***

** Gabatarwa ***

A cikin yanayin ci gaban software cikin sauri, manufar ZeroOps ta fito a matsayin tsarin juyin juya hali ga aikin sarrafa DevOps. ZeroOps yana nufin kawar da buƙatar sa hannun hannu a cikin ƙaddamarwa da sarrafa tsarin software, yana ba masu haɓaka damar mayar da hankali kan ayyuka masu girma kamar ƙira da ƙira. A cikin wannan labarin, za mu shiga cikin duniyar ZeroOps kuma mu bincika yadda za a iya amfani da wakilan AI da injiniyan mahallin don cimma wannan burin. Har ila yau, za mu tattauna aiwatar da tsarin da aka yi amfani da shi zuwa AI DevOps ta atomatik ta amfani da StackGen da LangChain.

Menene ZeroOps?

ZeroOps wani tsari ne na DevOps wanda ke neman sarrafa tsarin rayuwar ci gaban software gaba ɗaya, daga haɓakawa zuwa turawa da gudanarwa. Babban makasudin ZeroOps shine kawar da buƙatar sa hannun hannu a cikin turawa da sarrafa tsarin software, ta yadda za a rage yuwuwar kuskuren ɗan adam da haɓaka sauri da ingantaccen isar da software.

** Wakilan AI da Injiniyan Yanayi ***

Wakilan AI da injiniyan mahallin suna taka muhimmiyar rawa wajen aiwatar da ZeroOps. Wakilan AI shirye-shiryen software ne waɗanda za su iya fahimtar yanayin su kuma su ɗauki matakai don cimma takamaiman manufa. Injiniyan yanayi, a gefe guda, ya ƙunshi ƙira da aiwatar da tsarin da za su iya fahimta da amsa yanayin da suke aiki a ciki.

A cikin mahallin ZeroOps, ana iya amfani da wakilan AI don sarrafa ayyuka daban-daban kamar turawa, gwaji, da saka idanu. Ana iya amfani da injiniyan yanayi don tsara tsarin da zai iya fahimtar mahallin da suke aiki, kamar yanayi, mai amfani, da kuma aikin da ke hannunsu.

StackGen: Hanyar-Tsarin Tsari zuwa AI DevOps Automation

StackGen wani tsari ne na bude-bude wanda ke baiwa masu haɓaka damar aiwatar da tsarin tushen tsarin aiki da AI DevOps. StackGen yana ba da saiti na ƙira da ƙira waɗanda za a iya amfani da su don sarrafa ayyuka daban-daban kamar turawa, gwaji, da saka idanu.

An gina StackGen a saman LangChain, tsarin da ke ba masu haɓaka damar gina tsarin AI na tattaunawa. LangChain yana ba da saitin APIs da kayan aikin da za a iya amfani da su don gina tsarin AI na tattaunawa wanda zai iya fahimta da amsa abubuwan shigar da harshe na halitta.

** LangChain: Gina Tsarin AI na Tattaunawa ***

LangChain wani tsari ne wanda ke ba masu haɓaka damar gina tsarin AI na tattaunawa. LangChain yana ba da saitin APIs da kayan aikin da za a iya amfani da su don gina tsarin AI na tattaunawa wanda zai iya fahimta da amsa abubuwan shigar da harshe na halitta.

An gina LangChain a saman fasahar bude-bude da dama, gami da TensorFlow da PyTorch. LangChain yana ba da saitin APIs da kayan aikin da za a iya amfani da su don gina tsarin AI na tattaunawa wanda zai iya fahimta da amsa abubuwan shigar da harshe na halitta.

** Aiwatar da ZeroOps tare da StackGen da LangChain ***

Don aiwatar da ZeroOps tare da StackGen da LangChain, muna buƙatar bin matakai da yawa:

** Ƙayyade Wakilin AI ***: Mataki na farko na aiwatar da ZeroOps shine ayyana wakilin AI wanda za a yi amfani da shi don sarrafa ayyuka daban-daban. Ana iya siffanta wakilin AI ta amfani da adadin da aka riga aka siffanta alamu da samfuran da StackGen ke bayarwa.
** Zana Injin Magana ***: Mataki na gaba shine zayyana injin mahallin da za a yi amfani da shi don fahimta da amsa yanayin da wakilin AI ke aiki. Ana iya tsara injin mahallin ta amfani da adadin APIs da kayan aikin da LangChain ya bayar.
** Aiwatar da AI Agent ***: Da zarar an bayyana wakilin AI da injin mahallin, mataki na gaba shine aiwatar da wakilin AI ta amfani da adadin APIs da kayan aikin da StackGen da LangChain suka bayar.
** Gwaji da Ƙaddamarwa ***: Mataki na ƙarshe shine gwadawa da ƙaddamar da wakilin AI a cikin yanayin samarwa.

** Lambar Misali: Aiwatar da Sauƙaƙan Wakilin AI ta amfani da StackGen da LangChain ***

Anan akwai toshe lambar misali wanda ke nuna yadda ake aiwatar da wakilin AI mai sauƙi ta amfani da StackGen da LangChain: `` Python shigo da langchain daga langchain.chains shigo da LLMChain daga langchain.llms shigo da LLM

ai_agent = LLMChain( lm=LLM( model_name = "davinci: 3-batch", zafin jiki = 0.5, max_tokens=100, ), input_format = "rubutu", fitarwa_format = "rubutu", )

Ƙayyade injin mahallin

context_engine = langchain.ContextEngine( lm=LLM( model_name = "davinci: 3-batch", zafin jiki = 0.5, max_tokens=100, ), input_format = "rubutu", fitarwa_format = "rubutu", )

Aiwatar da wakilin AI

ai_agent.implement( context_engine = inji mai amfani, input_text="Sannu, ya kuke?", )

Gwaji da tura wakilin AI

ai_agent.test_da_deploy( input_text="Sannu, ya kuke?", )

Wannan toshe lambar yana nuna yadda ake aiwatar da wakilin AI mai sauƙi ta amfani da StackGen da LangChain. An ayyana wakili na AI ta amfani da adadin da aka riga aka siffanta alamu da samfuran da StackGen ya bayar. An tsara injin mahallin ta amfani da adadin APIs da kayan aikin da LangChain ya bayar. Ana aiwatar da wakilin AI ta amfani da adadin APIs da kayan aikin da StackGen da LangChain suka bayar. A ƙarshe, an gwada wakilin AI kuma an tura shi cikin yanayin samarwa.

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

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

1. `langchain.LLMChain()`: Yana haifar da sabon abu sarkar LLM.
2. `langchain.ContextEngine()': Yana ƙirƙira sabon abu injin mahallin.
3. `LLM (model_name = "davinci: 3-batch", zafin jiki = 0.5, max_tokens = 100)`: Yana ƙirƙira sabon abu na LLM tare da ƙayyadadden sunan samfurin, zafin jiki, da mafi girman alamu.
4. `ai_agent.implement(context_engine=context_engine, input_text="Sannu, ya kake?")`: Yana aiwatar da wakilin AI ta amfani da ƙayyadadden injin mahallin da shigar da rubutu.
5. `ai_agent.test_and_deploy(input_text = "Sannu, ya kuke?"): Gwaji da tura wakilin AI ta amfani da ƙayyadadden rubutun shigarwa.
6. `langchain.llms.LLM()': Yana haifar da sabon abu na LLM.
7. `langchain.chains.LLMChain()`: Yana haifar da sabon abu sarkar LLM.
8. `langchain.ContextEngine(llm=LLM(model_name=«davinci:3-batch»,zazzabi=0.5,max_tokens=100))`: Yana haifar da sabon mahallin injin abu ta amfani da takamaiman abin LLM.
9. `ai_agent.context_engine = langchain.ContextEngine(llm=LLM(model_name="davinci:3-batch",zazzabi=0.5,max_tokens=100))`: Yana saita mahallin mahallin ma'aikacin AI zuwa takamaiman abin injin mahallin.
10. `ai_agent.input_text = "Sannu, ya kuke?"': Yana saita rubutun shigar da wakilin AI zuwa takamaiman rubutun shigarwa.

**Kallon gani**

Anan ga hangen nesa na gine-ginen ZeroOps ta amfani da Chart.js:
Json
{
  "type": "bar",
  "data": {
    "labels": ["AI Agent", "Injin Magana", "StackGen", "LangChain"],
    "datasets": [
      {
        "label": "Architecture",
        "bayanai": [10, 8, 6, 4],
        "Bayan launi": ["#FF6384", "#36A2EB", "#FFCE56", "#4BC0C0"]
      }
    ]
  },
  "zaɓi": {
    "take": {
      "nuna": gaskiya,
      "text": "ZeroOps Architecture"
    },
    "tatsuniya": {
      "nuni": gaskiya
    },
    "ma'auni": {
      "yAxes": [
        {
          "zama": {
            "beginAtZero": gaskiya
          }
        }
      ]
    }
  }
}

Wannan hangen nesa yana nuna tsarin gine-ginen tsarin ZeroOps, gami da wakilin AI, injin mahallin, StackGen, da LangChain.

Kammalawa

ZeroOps hanya ce ta juyin juya hali ga DevOps aiki da kai wanda ke neman kawar da buƙatar sa hannun hannu a cikin turawa da sarrafa tsarin software. Ma'aikatan AI da injiniyan mahallin suna taka muhimmiyar rawa wajen aiwatar da ZeroOps, kuma StackGen da LangChain suna ba da tsarin tushen tsarin aiki na AI DevOps. Ta hanyar aiwatar da wakilin AI mai sauƙi ta amfani da StackGen da LangChain, za mu iya sarrafa ayyuka daban-daban kamar ƙaddamarwa, gwaji, da saka idanu. Hakanan ana bayar da manyan umarni 5-10 da aka fi amfani da su/snippets don StackGen da LangChain, tare da hangen nesa na gine-ginen ZeroOps ta amfani da Chart.js.

Buɗe ZeroOps tare da Agents AI da Injiniyan Mahimmanci: Tsarin Tushen Tsari zuwa AI DevOps Automation ta amfani da StackGen da LangChain

Ƙayyade injin mahallin

Aiwatar da wakilin AI

Gwaji da tura wakilin AI

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