ZeroOps: Kukwaniritsa Zomangamanga Zodziyimira ndi Ma AI Agents, StackGen, ndi LangChain
Chiyambi
M'malo omwe akusintha nthawi zonse a malo okhala ndi mitambo, kufunikira kwa kayendetsedwe kabwino ka zomangamanga sikunakhale kokwezeka. Zida zama automation zachikhalidwe zakhala zikuthandizira kuwongolera njira, koma nthawi zambiri zimalephera kuthana ndi zovuta komanso zovuta za zomangamanga zamakono. Apa ndipamene ZeroOps imabwera - njira yosinthira yomwe imathandizira Agetic AI kuti ikwaniritse kasamalidwe kazinthu zodziyimira pawokha. M'nkhaniyi, tifufuza za dziko la ZeroOps, ndikuwunika zigawo zake zazikulu, zopindulitsa, ndi ntchito zothandiza.
ZeroOps ndi chiyani?
ZeroOps ndi paradigm yomwe ikubwera yomwe imaphatikiza mphamvu ya Artificial Intelligence (AI) ndi kusinthasintha kwa malo okhala ndi mitambo kuti apange maziko odziyendetsa okha. Pogwiritsa ntchito luso la othandizira a AI, StackGen, ndi LangChain, ZeroOps imathandizira mabungwe kuti aziwongolera kasamalidwe kazinthu, kuchepetsa zolakwika za anthu, ndikuwongolera magwiridwe antchito.
**Agent AI: Mtima wa ZeroOps **
Agentik AI imatanthawuza mtundu wa AI womwe umawonetsa machitidwe anzeru, kuthekera kopanga zisankho, komanso kusinthasintha. Pankhani ya ZeroOps, ma Agents AI adapangidwa kuti aphunzire kuchokera kumayendedwe a zomangamanga, kuzindikira zolakwika, ndikuchitapo kanthu. Othandizirawa atha kuphunzitsidwa pazotengera zosiyanasiyana, kuphatikiza zolemba, ma metrics, ndi mafayilo osinthira, kuti amvetsetse mozama za machitidwe a zomangamanga.
StackGen: Injini ya Infrastructure-as-Code (IaC)
StackGen ndi injini yotseguka ya IaC yomwe imathandizira mabungwe kutanthauzira ndikuwongolera zomangamanga monga ma code. Pogwiritsa ntchito StackGen, omanga amatha kupanga ma tempuleti a zomangamanga, kutumiza mapulogalamu, ndikuwongolera zothandizira m'njira yosasinthika komanso yobwerezekanso. Kuphatikizika kwa StackGen ndi ma Agenti AI othandizira kumalola kuti pakhale malo odzichiritsa okha, pomwe othandizira amatha kuzindikira ndikukonza zovuta munthawi yeniyeni.
LangChain: The AI-Powered Infrastructure Orchestrator
LangChain ndi dongosolo la Python lomwe limathandiza omanga kupanga ndi kutumiza oimba a zomangamanga a AI. Pogwiritsa ntchito LangChain, mabungwe amatha kupanga othandizira a AI omwe amaphatikizana ndi StackGen ndi zida zina zogwirira ntchito kuti akwaniritse kayendetsedwe kazinthu zodziyimira pawokha. Zomangamanga za LangChain komanso laibulale yayikulu yazigawo zomwe zidamangidwa kale zimapangitsa kuti ikhale chisankho chabwino chomangira zovuta zamakina opangidwa ndi AI.
Kugwiritsa Ntchito: Chitsanzo Chosavuta
Tiyeni tilingalire chitsanzo chosavuta cha momwe ZeroOps ingagwiritsidwire ntchito mumtambo wachilengedwe. Tiyerekeze kuti tili ndi pulogalamu yapaintaneti yomwe yayikidwa pagulu la Kubernetes, ndipo tikufuna kusinthiratu njira yowonjezerera kutengera kuchuluka kwa magalimoto.
import langchain
kuchokera ku langchain.agents kuitanitsa LLMChain
kuchokera ku langchain.llms kuitanitsa LangChainLLM
# Tanthauzirani wothandizira wa AI
wothandizira = LLMChain (
llm=LangChainLLM(
model_name="code-davinci-002",
kutentha=0.7,
max_tokens=2048,
stop_sequences=["\n", "\n\n"]
),
chain_name = "scale_app"
)
# Tanthauzirani template ya StackGen
chitsanzo = {
"kind": "Kutumiza",
"metadata": {
"name": "web-app"
},
"spec": {
"Replicas": 1
}
}
# Fotokozani ndondomeko ya makulitsidwe
ndondomeko = {
"condition": "traffic > 1000",
"kuchita": {
"Replicas": 2
}
}
# Pangani woyimba zida zoyendetsedwa ndi AI
orchestrator = LangChainLLM (
model_name="code-davinci-002",
kutentha=0.7,
max_tokens=2048,
stop_sequences=["\n", "\n\n"]
)
# Tanthauzirani kutumizidwa kwa StackGen
kutumiza = StackGen (
template=chitsanzo,
mfundo=ndondomeko
)
# Ikani pulogalamuyi
deployment.deploy()
# Onjezani ntchito potengera kuchuluka kwa magalimoto
orchestrator.scale_app(wothandizira, kutumiza)
Muchitsanzo ichi, tafotokozera wothandizira wa AI wogwiritsa ntchito LangChain yemwe amalumikizana ndi StackGen kuti asinthe njira yowonjezerera pulogalamu yapaintaneti potengera kuchuluka kwa magalimoto. Wothandizira amagwiritsa ntchito chilankhulo chophunzitsidwa kale kuti asanthule zambiri zamagalimoto ndikupanga zisankho zakukulitsa pulogalamuyo.
**Malamulo/Zidutswa Zapamwamba za 5-10 **
Nawa ena mwa malamulo omwe amagwiritsidwa ntchito kwambiri ndi mawu a ZeroOps:
- langchain init: Imayambitsa ntchito yatsopano ya LangChain.
langchain init my_project
````
2. **llm unyolo**: Amapanga LLM yatsopano.
``` python
kuchokera ku langchain.agents kuitanitsa LLMChain
kuchokera ku langchain.llms kuitanitsa LangChainLLM
wothandizira = LLMChain (
llm=LangChainLLM(
model_name="code-davinci-002",
kutentha=0.7,
max_tokens=2048,
stop_sequences=["\n", "\n\n"]
),
chain_name="my_chain"
)
````
3. **stackgen deploy**: Imatumiza template ya StackGen kwa opereka mtambo.
``````chosa
stackgen deploy my_template.yaml
````
4. **orchestrator scale_app**: Imachulukitsa pulogalamu potengera kuchuluka kwa magalimoto.
``` python
kuchokera ku langchain.agents kuitanitsa LLMChain
kuchokera ku langchain.llms kuitanitsa LangChainLLM
orchestrator = LangChainLLM (
model_name="code-davinci-002",
kutentha=0.7,
max_tokens=2048,
stop_sequences=["\n", "\n\n"]
)
orchestrator.scale_app(wothandizira, kutumiza)
````
5. **sitima ya wothandizira**: Amaphunzitsa wothandizira AI pa dataset.
``````chosa
wothandizira phunzitsani my_dataset.csv
````
6. **ndondomeko ya stackgen**: Imatanthauzira ndondomeko ya makulitsidwe a ntchito.
``` python
ndondomeko = {
"condition": "traffic > 1000",
"kuchita": {
"Replicas": 2
}
}
````
7. **langchain visualize**: Imawonera m'maganizo njira yopangira zisankho za wothandizira wa AI.
``````chosa
langchain kuona my_agent.json
````
8. **mawonekedwe a oimba **: Imawonetsa mawonekedwe a pulogalamu.
``````chosa
orchestrator status my_app
````
9. **zipika za stackgen **: Zimasonyeza zipika za kutumizidwa kwa StackGen.
``````chosa
stackgen logs my_deployment
````
10. **wothandizira awunika**: Amawunika momwe a AI amathandizira.
``````chosa
wothandizira amawunika my_agent.json
````
**Kuwona **
Nayi chithunzithunzi cha kamangidwe ka ZeroOps pogwiritsa ntchito Chart.js:
```json
{
"type": "bar",
"data": {
"labels": ["Agent AI", "StackGen", "LangChain"],
"ma dataset": [
{
"label": "Components",
"data": [10, 8, 12],
"backgroundColor": [
"rgba(255, 99, 132, 0.2)",
"rgba(54, 162, 235, 0.2)",
"rgba(255, 206, 86, 0.2)"
],
"BorderColor": [
"rgba(255, 99, 132, 1)",
"rgba(54, 162, 235, 1)",
"rgba(255, 206, 86, 1)"
],
"BorderWidth": 1
}
]
},
"zosankha": {
"masikelo": {
"yAxes": [
{
"nkhupakupa": {
"beginAtZero": zoona
}
}
]
}
}
}
````
Kuwoneka uku kukuwonetsa zigawo zitatu zazikuluzikulu za ZeroOps: Agent AI, StackGen, ndi LangChain. Tchati cha bar chikuwonetsa kufunikira kwa gawo lililonse pamapangidwe a ZeroOps.
**Mapeto**
ZeroOps ndi njira yosinthira kasamalidwe kazinthu zomwe zimathandizira Agent AI, StackGen, ndi LangChain kuti akwaniritse kasamalidwe kazinthu zodziyimira pawokha. Pogwiritsa ntchito mphamvu za AI ndi IaC, ZeroOps imathandizira mabungwe kuti aziwongolera kasamalidwe kazinthu, kuchepetsa zolakwika za anthu, ndikuwongolera magwiridwe antchito. Ndi kugwiritsa ntchito kwake, malamulo apamwamba, ndi zowonera, ZeroOps ili pafupi kusintha momwe timayendetsera zomangamanga m'malo okhala ndi mitambo.