** ZeroOps: Aṣeyọri Awọn amayederun adase pẹlu Awọn Aṣoju AI, StackGen, ati LangChain ***

Ibere

Ni ilẹ-ilẹ ti o n dagba nigbagbogbo ti awọn agbegbe abinibi-awọsanma, ibeere fun ṣiṣe daradara ati iṣakoso amayederun ti iwọn ko ti ga julọ. Awọn irinṣẹ adaṣe adaṣe ti aṣa ti jẹ ohun elo ni ṣiṣatunṣe awọn ilana, ṣugbọn wọn nigbagbogbo kuna kukuru ni sisọ awọn idiju ati awọn nuances ti awọn amayederun ode oni. Eyi ni ibiti ZeroOps ti n wọle - ọna rogbodiyan ti o mu Agentic AI ṣiṣẹ lati ṣaṣeyọri iṣakoso amayederun adase. Ninu nkan yii, a yoo lọ sinu agbaye ti ZeroOps, ṣawari awọn paati pataki rẹ, awọn anfani, ati awọn ohun elo to wulo.

Kini ZeroOps?

ZeroOps jẹ apẹrẹ ti o nwaye ti o dapọ agbara ti Artificial Intelligence (AI) pẹlu irọrun ti awọn agbegbe abinibi-awọsanma lati ṣẹda awọn amayederun iṣakoso ti ara ẹni. Nipa lilo awọn agbara ti awọn aṣoju AI, StackGen, ati LangChain, ZeroOps n fun awọn ajo laaye lati ṣe adaṣe iṣakoso amayederun, dinku aṣiṣe eniyan, ati ilọsiwaju ṣiṣe gbogbogbo.

** AI Aṣoju: Ọkàn ZeroOps ***

AI Agenttic tọka si iru AI ti o ṣe afihan ihuwasi oye, awọn agbara ṣiṣe ipinnu, ati ibaramu. Ninu ọrọ ti ZeroOps, awọn aṣoju AI ti ṣe apẹrẹ lati kọ ẹkọ lati awọn ilana amayederun, ṣe idanimọ awọn aiṣedeede, ati ṣe awọn iṣe atunṣe. Awọn aṣoju wọnyi le ṣe ikẹkọ lori ọpọlọpọ awọn orisun data, pẹlu awọn akọọlẹ, awọn metiriki, ati awọn faili atunto, lati ṣe idagbasoke oye ti o jinlẹ ti ihuwasi amayederun.

**StackGen: Awọn amayederun-bi-koodu (IaC) Ẹrọ ***

StackGen jẹ ẹrọ orisun orisun IaC ti o fun laaye awọn ajo lati ṣalaye ati ṣakoso awọn amayederun bi koodu. Nipa lilo StackGen, awọn olupilẹṣẹ le ṣẹda awọn awoṣe amayederun, ran awọn ohun elo ṣiṣẹ, ati ṣakoso awọn orisun ni ọna deede ati atunṣe. Iṣọkan StackGen pẹlu awọn aṣoju AI Agentic ngbanilaaye fun ṣiṣẹda awọn amayederun imularada ti ara ẹni, nibiti awọn aṣoju le rii ati ṣatunṣe awọn ọran ni akoko gidi.

** LangChain: Orchestrator Amayederun Agbara AI-Agbara ***

LangChain jẹ ilana ti o da lori Python ti o fun laaye awọn olupilẹṣẹ lati kọ ati ran awọn akọrin amayederun agbara AI ṣiṣẹ. Nipa gbigbe LangChain ṣiṣẹ, awọn ajo le ṣẹda awọn aṣoju AI aṣa ti o ṣepọ pẹlu StackGen ati awọn irinṣẹ amayederun miiran lati ṣaṣeyọri iṣakoso amayederun adase. Iṣatunṣe apọjuwọn LangChain ati ile-ikawe lọpọlọpọ ti awọn paati ti a ti kọ tẹlẹ jẹ ki o jẹ yiyan ti o dara julọ fun kikọ awọn solusan amayederun agbara AI eka.

** Ohun elo Iṣe: Apẹẹrẹ Rọrun ***

Jẹ ki a wo apẹẹrẹ ti o rọrun ti bii ZeroOps ṣe le lo ni agbegbe abinibi-awọsanma. Ṣebi a ni ohun elo wẹẹbu kan ti a gbe sori iṣupọ Kubernetes, ati pe a fẹ lati ṣe adaṣe ilana ti iwọn ohun elo ti o da lori ibeere ijabọ.

`` Python

gbe wọle langchain lati langchain.agents gbe wọle LLMChain lati langchain.llms gbe wọle LangChainLLM

Ṣe alaye aṣoju AI

aṣoju = LLMChain ( llm=LangChainLLM( model_name = "koodu-davinci-002", otutu = 0.7, max_tokens=2048, stop_sequences=["\n", "\n\n"] ), chain_name = "iwọn_app" )

Ṣetumo awoṣe StackGen

awoṣe = { "kind": "Ìfilọṣẹ", "metadata": { "name": "app-ayelujara" }, "ni pato": { "awọn ẹda": 1 } }

Ṣetumo eto imulo iwọn

ilana = { "condition": "ijabọ> 1000", "igbese": { "awọn ẹda": 2 } }

Ṣẹda orchestrator amayederun agbara AI

akọrin = LangChainLLM( model_name = "koodu-davinci-002", otutu = 0.7, max_tokens=2048, stop_sequences=["\n", "\n\n"] )

Ṣetumo imuṣiṣẹ StackGen

imuṣiṣẹ = StackGen ( awoṣe=awoṣe, imulo = imulo )

Mu ohun elo naa ṣiṣẹ

imuṣiṣẹ.deploy ()

Ṣe iwọn ohun elo ti o da lori ibeere ijabọ

orchestrator.scale_app (aṣoju, imuṣiṣẹ)


Ni apẹẹrẹ yii, a ti ṣalaye aṣoju AI kan nipa lilo LangChain ti o ṣepọ pẹlu StackGen lati ṣe adaṣe ilana ti iwọn ohun elo wẹẹbu kan ti o da lori ibeere ijabọ. Aṣoju naa nlo awoṣe ede ti a ti kọ tẹlẹ lati ṣe itupalẹ data ijabọ ati ṣe awọn ipinnu nipa iwọn ohun elo naa.

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

Eyi ni diẹ ninu awọn aṣẹ ti o wọpọ julọ ati awọn snippets fun ZeroOps:

1. ** langchain init ***: Ti bẹrẹ iṣẹ LangChain tuntun kan.
`` bash
langchain init my_project

** lm pq ***: Ṣẹda titun kan LLM pq. `` Python lati langchain.agents gbe wọle LLMChain lati langchain.llms gbe wọle LangChainLLM

aṣoju = LLMChain ( llm=LangChainLLM( model_name = "koodu-davinci-002", otutu = 0.7, max_tokens=2048, stop_sequences=["\n", "\n\n"] ), chain_name = "my_chain" )

3. ** stackgen ransogun ***: Rans a StackGen awoṣe to a awọsanma olupese.
`` bash
stackgen ran my_template.yaml

** Orchestrator scale_app ***: Ṣe iwọn ohun elo kan ti o da lori ibeere ijabọ. `` Python lati langchain.agents gbe wọle LLMChain lati langchain.llms gbe wọle LangChainLLM

akọrin = LangChainLLM( model_name = "koodu-davinci-002", otutu = 0.7, max_tokens=2048, stop_sequences=["\n", "\n\n"] )

orchestrator.scale_app (aṣoju, imuṣiṣẹ)

5. ** ọkọ oju irin aṣoju ***: Ṣe ikẹkọ aṣoju AI kan lori dataset kan.
`` bash
oluranlowo reluwe my_dataset.csv

** stackgen imulo ***: asọye a igbelosoke imulo fun ohun elo. `` Python ilana = { "condition": "ijabọ> 1000", "igbese": { "awọn ẹda": 2 } }

7. ** langchain visualize ***: Ṣe akiyesi ilana ṣiṣe ipinnu oluranlowo AI kan.
`` bash
langchain visualize my_agent.json

** ipo orchestrator ***: Ṣe afihan ipo ohun elo kan. `` bash ipo orchestrator my_app

9. ** stackgen àkọọlẹ ***: Han awọn àkọọlẹ ti a StackGen imuṣiṣẹ.
`` bash
stackgen àkọọlẹ my_deployment

** aṣoju iṣiro ***: Ṣe iṣiro iṣẹ ti oluranlowo AI. `` bash oluranlowo akojopo my_agent.json


** Iworan ***

Eyi ni iwoye ti faaji ZeroOps nipa lilo Chart.js:
`` json
{
  "type": "ọgọ",
  "data": {
    "aami": ["Agentic AI", "StackGen", "LangChain"],
    "awọn ipilẹ data": [
      {
        "label": "Components",
        "data": [10, 8, 12],
        "awọ abẹlẹ": [
          "rgba (255, 99, 132, 0.2)",
          "rgba (54, 162, 235, 0.2)",
          "rgba (255, 206, 86, 0.2)"
        ],
        "Awọ aala": [
          "rgba (255, 99, 132, 1)",
          "rgba (54, 162, 235, 1)",
          "rgba (255, 206, 86, 1)"
        ],
        "AalaWidth": 1
      }
    ]
  },
  "awọn aṣayan": {
    "iwọn": {
      "yAxes": [
        {
          "awọn ami": {
            "beginAtZero": otitọ
          }
        }
      ]
    }
  }
}

Iworan yii fihan awọn paati pataki mẹta ti ZeroOps: AI Agentic, StackGen, ati LangChain. Apẹrẹ igi ṣe afihan pataki ibatan ti paati kọọkan ninu faaji ZeroOps.

Ipari

ZeroOps jẹ ọna rogbodiyan si iṣakoso amayederun ti o mu Agentic AI, StackGen, ati LangChain ṣiṣẹ lati ṣaṣeyọri iṣakoso amayederun adase. Nipa lilo agbara AI ati IaC, ZeroOps n fun awọn ajo laaye lati ṣe adaṣe iṣakoso amayederun, dinku aṣiṣe eniyan, ati ilọsiwaju ṣiṣe gbogbogbo. Pẹlu awọn ohun elo ti o wulo, awọn aṣẹ oke, ati iworan, ZeroOps ti ṣetan lati yi ọna ti a ṣakoso awọn amayederun ni awọn agbegbe abinibi-awọsanma.

ZeroOps: Aṣeyọri Awọn amayederun adase pẹlu Awọn Aṣoju AI, StackGen, ati LangChain