ZeroOps: Ausia Atinae Tutoatasi ma AI Agents, StackGen, ma LangChain | B4N1

ZeroOps: Ausia Atinae Tutoatasi ma AI Agents, StackGen, ma LangChain

7 months ago · b4n1 Read time: 5 min

ZeroOps: Ausia Atinae Tutoatasi ma AI Agents, StackGen, ma LangChain

Fa'atomuaga

I le fa'asolosolo fa'asolosolo o si'osi'omaga o ao, o le mana'oga mo le fa'aleleia lelei ma le fa'aogaina o atina'e e le'i maualuga lava. O mea faigaluega fa'a-automai fa'aleaganu'u sa fa'aogaina i le fa'afaigofieina o fa'agasologa, ae e masani ona fa'aletonu i le fa'atalanoaina o le lavelave ma fa'alavelave o atina'e fa'aonaponei. O le mea lea e sau ai ZeroOps - o se faiga fou e faʻaaogaina ai le Agentic AI e ausia ai pulega tutoʻatasi. I lenei tusiga, o le a tatou suʻesuʻeina le lalolagi o ZeroOps, suʻesuʻeina ona vaega autu, faʻamanuiaga, ma faʻaoga aoga.

O le a le ZeroOps?

ZeroOps o se faʻataʻitaʻiga faʻaalia lea e tuʻufaʻatasia ai le malosi o le Artificial Intelligence (AI) ma le fetuutuunai o siosiomaga faʻapitonuʻu o le ao e fausia ai atinaʻe e pulea e le tagata lava ia. E ala i le faʻaogaina o agavaʻa o sui o AI, StackGen, ma LangChain, ZeroOps e mafai ai e faʻalapotopotoga ona faʻatautaia le faʻatautaia o atinaʻe, faʻaitiitia le sese o tagata, ma faʻaleleia le lelei atoatoa.

Agentic AI: Le Loto o ZeroOps

Agentic AI o loʻo faʻatatau i se ituaiga o AI e faʻaalia ai le amio atamai, gafatia fai filifiliga, ma le fetuutuunai. I le tulaga o ZeroOps, Agentic AI agents ua mamanuina e aʻoaʻo mai faʻataʻitaʻiga fausaga, faʻailoa faʻalavelave, ma faia ni faʻasaʻoga. O nei sooupu e mafai ona aʻoaʻoina i punaoa faʻamatalaga eseese, e aofia ai ogalaau, metrics, ma faila faʻatulagaina, e atiaʻe ai se malamalama loloto i amioga a le atinaʻe.

StackGen: Le Inisinia Infrastructure-as-Code (IaC)

O le StackGen o se IaC engine e mafai ai e faʻalapotopotoga ona faʻamalamalamaina ma faʻatautaia mea tetele e fai ma tulafono. I le faʻaaogaina o StackGen, e mafai e le au atinaʻe ona fatuina faʻataʻitaʻiga faʻapitoa, faʻapipiʻi talosaga, ma faʻatautaia punaoa i se faiga faifaipea ma toe faʻaleleia. O le tuʻufaʻatasia a StackGen ma sui sooupu Agentic AI e mafai ai ona fausia ni atinaʻe faʻamalolo, lea e mafai ai e sui ona iloa ma faʻasaʻo faʻafitauli i le taimi moni.

LangChain: Le AI-Powered Infrastructure Orchestrator

LangChain o se faʻavae faʻavae Python lea e mafai ai e le au atinaʻe ona fausia ma faʻapipiʻi AI-powered infrastructure orchesttors. E ala i le faʻaaogaina o LangChain, e mafai e faʻalapotopotoga ona fausia ni faʻalapotopotoga faʻapitoa AI e tuʻufaʻatasia ma StackGen ma isi meafaigaluega faʻapitoa e ausia ai pulega tutoʻatasi. O le fausaga faʻapitoa a le LangChain ma le tele o faletusi o vaega na muai fausia e avea ma filifiliga sili mo le fausiaina o faʻafitauli faʻapitoa e faʻaogaina e AI.

Talosaga Fa'atino: O se Fa'ata'ita'iga Faigofie

Sei o tatou mafaufau i se faʻataʻitaʻiga faigofie o le auala e mafai ona faʻaogaina ai ZeroOps i totonu ole siosiomaga faʻaonaponei. Fa'apea o lo'o i ai sa matou 'upega tafa'ilagi o lo'o fa'apipi'iina i luga o le Kubernetes cluster, ma matou te manana'o e fa'autometi le fa'agasologa o le fa'avasegaina o le talosaga e fa'atatau i le mana'omia o felauaiga.


faaulufale mai le langchain
mai langchain.agents import LLMChain
mai langchain.llms faaulufale mai LangChainLLM

# Fa'amatala le sui sooupu AI
sooupu = LLMChain(
    llm=LangChainLLM(
        model_name="code-davinci-002",
        vevela=0.7,
        max_tokens=2048,
        stop_sequences=["\n", "\n\n"]
    ),
    chain_name = "fuaina_app"
)

# Fa'amatala le mamanu StackGen
mamanu = {
    "kind": "Fa'atonu",
    "metadata": {
        "igoa": "web-app"
    },
    "fa'amatalaga": {
        "faatusa": 1
    }
}

# Fa'amatala le faiga fa'avae
faiga fa'avae = {
    "condition": "ta'avale > 1000",
    "gaioiga": {
        "faatusa": 2
    }
}

# Fausia le faʻatonu faʻapitoa e faʻamalosia ai le AI
fa'atutusa = LangChainLLM(
    model_name="code-davinci-002",
    vevela=0.7,
    max_tokens=2048,
    stop_sequences=["\n", "\n\n"]
)

# Fa'amatala le fa'aogaina o le StackGen
fa'aogaina = StackGen(
    fa'ata'ita'iga = template,
    policy=policy
)

# Faʻapipiʻi le talosaga
deployment.deploy()

# Faʻateleina le talosaga e faʻavae i luga o manaʻoga tau feoaiga
orchestrator.scale_app(suesue, fa'agaioiga)

I lenei faʻataʻitaʻiga, ua matou faʻamalamalamaina se sui AI e faʻaaoga LangChain e tuʻufaʻatasia ma StackGen e faʻatautaia ai le faʻagasologa o le faʻaogaina o se upega tafaʻilagi e faʻavae i luga o manaoga tau feoaiga. E fa'aaoga e le sooupu se fa'ata'ita'iga gagana a'o le'i a'oa'oina e iloilo ai fa'amaumauga o femalagaiga ma fai fa'ai'uga e uiga i le fa'asiliina o le talosaga.

Poloaiga/Snippets pito i luga 5-10 Fa'aaogaina

O nisi nei o tulafono e masani ona faʻaaogaina ma snippet mo ZeroOps:

  1. langchain init: Amataina se galuega fou LangChain.
langchain init my_project
  1. llm filifili: Fausia se filifili LLM fou.
mai langchain.agents import LLMChain
mai langchain.llms faaulufale mai LangChainLLM

sooupu = LLMChain(
    llm=LangChainLLM(
        model_name="code-davinci-002",
        vevela=0.7,
        max_tokens=2048,
        stop_sequences=["\n", "\n\n"]
    ),
    filifili_igoa="my_chain"
)
  1. stackgen deploy: Tu'u atu se ata StackGen i se fa'asoa ao.
stackgen deploy my_template.yaml
  1. orchestrator scale_app: Fa'afua se talosaga e fa'atatau ile mana'oga tau feoaiga.
mai langchain.agents import LLMChain
mai langchain.llms faaulufale mai LangChainLLM

fa'atutusa = LangChainLLM(
    model_name="code-davinci-002",
    vevela=0.7,
    max_tokens=2048,
    stop_sequences=["\n", "\n\n"]
)

orchestrator.scale_app(suesue, fa'agaioiga)
  1. agent train: Toleniina se sui sooupu AI i luga o se faamaumauga.
sui sooupu my_dataset.csv
  1. stackgen policy: Fa'amatala se faiga fa'avae mo se talosaga.
faiga fa'avae = {
    "condition": "ta'avale > 1000",
    "gaioiga": {
        "faatusa": 2
    }
}
  1. langchain visualize: Va'aia le faiga o filifiliga a se sui sooupu AI.
langchain vaai faalemafaufau my_agent.json
  1. Tulaga o le faomea: Fa'aali le tulaga o se talosaga.
tulaga o le aufaipese my_app
  1. stackgen logs: Fa'aali atu ogalaau o se fa'aogaina o le StackGen.
stackgen logs my_deployment
  1. suesue sui: Iloilo le faatinoga o se sui sooupu AI.
sui sooupu iloilo my_agent.json

Va'aiga

Ole fa'aaliga lea ole fausaga ole ZeroOps ile fa'aogaina ole Chart.js:

{
  "type": "pa",
  "fa'amatalaga": {
    "labels": ["Agentic AI", "StackGen", "LangChain"],
    "fa'amaumauga": [
      {
        "label": "Igoa",
        "faamatalaga": [10, 8, 12],
        "Lulu lanu": [
          "rgba(255, 99, 132, 0.2)",
          "rgba(54, 162, 235, 0.2)",
          "rgba(255, 206, 86, 0.2)"
        ],
        "Lulu tuaoi": [
          "rgba(255, 99, 132, 1)",
          "rgba(54, 162, 235, 1)",
          "rgba(255, 206, 86, 1)"
        ],
        "BorderWidth": 1
      }
    ]
  },
  "filifiliga": {
    "fua": {
      "yAxes": [
        {
          "tiki": {
            "beginAtZero": moni
          }
        }
      ]
    }
  }
}

O lenei faʻaaliga o loʻo faʻaalia ai vaega autu e tolu o ZeroOps: Agentic AI, StackGen, ma LangChain. O le siata o le pa o loʻo faʻaalia ai le taua tele o vaega taʻitasi i le fausaga o ZeroOps.

Fa'ai'uga

ZeroOps o se faiga faʻafouina i pulega faʻapitoa e faʻaaogaina ai Agentic AI, StackGen, ma LangChain e ausia ai pulega tutoʻatasi. E ala i le faʻaogaina o le mana o AI ma IaC, ZeroOps e mafai ai e faʻalapotopotoga ona faʻaogaina le faʻaogaina o mea tetele, faʻaitiitia le sese o tagata, ma faʻaleleia le lelei atoatoa. Faatasi ai ma ana faʻaoga faʻaoga, faʻatonuga pito i luga, ma faʻaaliga, ZeroOps ua sauni e suia le auala tatou te faʻatautaia ai atinaʻe i totonu ole siosiomaga masani.

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