ZeroOps: Aṣeyọri iṣakoso Awọn amayederun adase pẹlu Awọn aṣoju AI ati Awọn irinṣẹ Orchestration | B4N1

ZeroOps: Aṣeyọri iṣakoso Awọn amayederun adase pẹlu Awọn aṣoju AI ati Awọn irinṣẹ Orchestration

7 months ago · b4n1 Read time: 5 min

Ifihan

Ni ala-ilẹ oni-nọmba ti o yara ti ode oni, iṣakoso awọn amayederun ti di iṣẹ ṣiṣe ti n gba akoko ati eka. Pẹlu igbega ti iṣiro awọsanma, ifipamọ, ati awọn iṣẹ microservices, ọna DevOps ti aṣa si iṣakoso amayederun ti di nija siwaju sii. Eyi ni ibiti ZeroOps wa - ọna iyipada si iṣakoso amayederun ti o nmu awọn aṣoju AI ati awọn irinṣẹ orchestration ṣiṣẹ lati ṣẹda iwosan ara ẹni, awọn amayederun ti o dara julọ.

Ninu nkan yii, a yoo ṣawari ikorita ti AI, DevOps, ati ZeroOps lati ṣẹda iṣakoso amayederun adase nipa lilo LangChain, DSPy, ati StackGen. A yoo ṣawari sinu awọn abala iṣe ti imuse ZeroOps, pẹlu awọn apẹẹrẹ koodu, awọn atokọ aṣẹ, ati iworan.

Kini ZeroOps?

ZeroOps jẹ ayipada paragim ni iṣakoso amayederun ti o ni ero lati yọkuro iwulo fun ilowosi eniyan ni ṣiṣakoso awọn amayederun. O ṣaṣeyọri eyi nipa jijẹ awọn aṣoju AI ati awọn irinṣẹ orchestration lati ṣe adaṣe awọn iṣẹ ṣiṣe bii ibojuwo, iwọn, ati laasigbotitusita.

Ero pataki ti o wa lẹhin ZeroOps ni lati ṣẹda imularada ti ara ẹni, awọn amayederun ti o dara ju ti ara ẹni ti o le ṣe deede si iyipada awọn iṣẹ ṣiṣe ati awọn agbegbe laisi ilowosi eniyan. Eyi jẹ aṣeyọri nipasẹ lilo awọn aṣoju AI lati ṣe itupalẹ data lati awọn orisun oriṣiriṣi, ṣe idanimọ awọn ilana, ati ṣe awọn ipinnu lati mu iṣẹ ṣiṣe amayederun dara si.

Awọn aṣoju AI ati Awọn irinṣẹ Orchestration

Awọn aṣoju AI ati awọn irinṣẹ orchestration jẹ ẹhin ti ZeroOps. Awọn aṣoju AI jẹ awọn eto sọfitiwia ti o lo algorithms ẹkọ ẹrọ lati ṣe itupalẹ data ati ṣe awọn ipinnu. Awọn irinṣẹ orchestration, ni apa keji, ni a lo lati ṣe adaṣe awọn iṣẹ ṣiṣe bii imuṣiṣẹ, iwọn, ati ibojuwo.

Diẹ ninu awọn aṣoju AI olokiki ati awọn irinṣẹ orchestration ti a lo ninu ZeroOps pẹlu:

  • LangChain: Ile-ikawe Python kan fun kikọ awọn aṣoju AI ibaraẹnisọrọ.
  • DSPy: Ile-ikawe Python kan fun kikọ awọn opo gigun ti imọ-jinlẹ data.
  • StackGen: Ọpa kan fun adaṣe imuṣiṣẹ amayederun ati iṣakoso.

Ṣiṣe ZeroOps pẹlu LangChain, DSPy, ati StackGen

Ni apakan yii, a yoo pese apẹẹrẹ ti o wulo ti imuse ZeroOps nipa lilo LangChain, DSPy, ati StackGen.

Igbesẹ 1: Ṣiṣeto Ayika naa

Lati bẹrẹ, a nilo lati ṣeto agbegbe wa pẹlu LangChain, DSPy, ati StackGen. A le fi awọn ile-ikawe wọnyi sori ẹrọ ni lilo pip: `` bash pip fi sori ẹrọ langchain dspy stackgen

### Igbesẹ 2: Ṣiṣe Aṣoju AI

Nigbamii ti, a nilo lati kọ oluranlowo AI wa nipa lilo LangChain. A le ṣẹda aṣoju ti o rọrun ti o nlo sisẹ ede adayeba (NLP) lati ṣe itupalẹ awọn akọọlẹ ati ṣe idanimọ awọn ilana:
`` Python
lati agbewọle langchain LLAMA

# Ṣẹda aṣoju LLAMA kan
aṣoju = LLAMA(
    model_name = "xlarge",
    max_length=1024,
    otutu = 0.5,
)

# Ṣetumo iṣẹ kan lati ṣe itupalẹ awọn akọọlẹ
def analyze_logs(awọn akọọlẹ):
    # Lo aṣoju lati ṣe itupalẹ awọn akọọlẹ naa
    iṣẹjade = agent.run (awọn akọọlẹ)
    pada jade

# Ṣe idanwo iṣẹ naa
logs = ["Aṣiṣe: Ti pari akoko asopọ", "Aṣiṣe: Isopọ aaye data kuna"]
iṣẹjade = analyze_logs(awọn akọọlẹ)
titẹ (jade)

Igbesẹ 3: Ṣiṣe Irinṣẹ Orchestration

Nigbamii ti, a nilo lati kọ ohun elo orchestration wa nipa lilo StackGen. A le ṣẹda ohun elo ti o rọrun ti o ṣe adaṣe imuṣiṣẹ ati iwọn: `` Python lati stackgen gbe wọle StackGen

Ṣẹda apẹẹrẹ StackGen

stackgen = StackGen()

Ṣetumo iṣẹ kan lati ran ati iwọn

def deploy_and_scale(): # Lo StackGen lati ran ati iwọn stackgen.deploy_and_scale () pada

Ṣe idanwo iṣẹ naa

ransiṣẹ_ati_scale()

### Igbesẹ 4: Iṣajọpọ Aṣoju AI ati Irinṣẹ Orchestration

Nikẹhin, a nilo lati ṣepọ oluranlowo AI wa ati ọpa orchestration lati ṣẹda eto ZeroOps kan. A le lo DSPy lati ṣẹda opo gigun ti imọ-jinlẹ data ti o ṣepọ awọn meji:
`` Python
lati dspy agbewọle Pipeline

# Ṣẹda opo gigun ti epo
opo = opopo ()

# Ṣafikun aṣoju AI si opo gigun ti epo
pipeline.add_task(analyze_logs)

# Ṣafikun ohun elo orchestration si opo gigun ti epo
pipeline.add_task (deploy_and_scale)

# Ṣiṣe opo gigun ti epo
pipeline.run ()

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

Eyi ni awọn aṣẹ 5-10 ti o lo julọ / awọn snippets fun ZeroOps:

  1. pip fi sori ẹrọ langchain dspy stackgen: Fi LangChain, DSPy, ati StackGen sori ẹrọ.
  2. lati langchain agbewọle LLAMA: gbe wọle oluranlowo LLAMA lati LangChain.
  3. aṣoju = LLAMA(...): Ṣẹda aṣoju LLAMA kan.
  4. def analyze_logs(logs): ...: Ṣetumo iṣẹ kan lati ṣe itupalẹ awọn akọọlẹ nipa lilo aṣoju AI.
  5. lati stackgen gbe wọle StackGen: Gbe StackGen apẹẹrẹ lati StackGen.
  6. stackgen = StackGen (): Ṣẹda a StackGen apẹẹrẹ.
  7. def deploy_and_scale (): ...: Ṣetumo iṣẹ kan lati ran ati iwọn lilo StackGen.
  8. lati dspy agbewọle Pipeline: Gbe awọn Pipeline kilasi lati DSPy.
  9. Pipeline = Pipeline (): Ṣẹda opo gigun ti epo.
  10. pipeline.add_task(analyze_logs): Fi oluranlowo AI kun opo gigun ti epo.

Wiwo

Eyi ni iwoye ti eto ZeroOps nipa lilo Chart.js: `` json { "type": "ọgọ", "data": { "labels": ["Aṣojú AI", "Ọpa Orchestration", "Pipiline Imọ Data"], "awọn ipilẹ data": [ { "label": "Eto ZeroOps", "data": [10, 20, 30], "backgroundColor": ["#4CAF50", "#FF9800", "#2196F3"] } ] }, "awọn aṣayan": { "akọle": { "ifihan": otitọ, "ọrọ": "Awọn ohun elo eto ZeroOps" }, "iwọn": { "yAxes": [ { "awọn ami": { "beginAtZero": otitọ } } ] } } }

Iwoye yii fihan awọn paati akọkọ mẹta ti eto ZeroOps: aṣoju AI, ohun elo orchestration, ati opo gigun ti imọ-jinlẹ data.

## Ipari

ZeroOps jẹ ọna rogbodiyan si iṣakoso amayederun ti o mu awọn aṣoju AI ṣiṣẹ ati awọn irinṣẹ orchestration lati ṣẹda imularada ti ara ẹni, awọn amayederun ti o dara julọ. Nipa lilo LangChain, DSPy, ati StackGen, a le kọ eto ZeroOps kan ti o ṣe adaṣe awọn iṣẹ ṣiṣe bii ibojuwo, iwọn, ati laasigbotitusita.

Ninu àpilẹkọ yii, a ti pese apẹẹrẹ ti o wulo ti imuse ZeroOps nipa lilo LangChain, DSPy, ati StackGen. A tun ti pese iworan ti eto ZeroOps nipa lilo Chart.js.

A nireti pe nkan yii ti pese akopọ okeerẹ ti ZeroOps ati agbara rẹ lati yi ọna ti a ṣakoso awọn amayederun pada.

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