Kuvhura ZeroOps neAI Agents uye Context Engineering: Iyo Pateni-Yakavakirwa Nzira yeAI DevOps Automation uchishandisa StackGen uye LangChain.

Kuvhura ZeroOps neAI Agents uye Context Injiniya: Iyo Pateni-Yakavakirwa Nzira yeAI DevOps Automation uchishandisa StackGen uye LangChain

Introduction

Munzvimbo inokurumidza kubuda yekuvandudza software, iyo pfungwa yeZeroOps yabuda senzira yekuchinja kuDevOps otomatiki. ZeroOps ine chinangwa chekubvisa kudiwa kwekupindira kwemaoko mukuendesa uye manejimendi emasoftware masisitimu, zvichiita kuti vanogadzira vatarise pamabasa epamusoro-soro akadai sekuvandudza uye kugadzira. Muchinyorwa chino, isu tichapinda munyika yeZeroOps uye tiongorore kuti maAI vamiririri uye mamiriro einjiniya anogona kukwidziridzwa kuti azadzise chinangwa ichi. Tichakurukurawo nezvekushandiswa kwemaitiro-based approach kuAI DevOps automation uchishandisa StackGen uye LangChain.

Chii chinonzi ZeroOps?

ZeroOps iDevOps paradigm inotsvaga kuita otomatiki iyo yese software yekuvandudza hupenyu, kubva kubudiriro kuenda kukuendesa uye manejimendi. Chinangwa chikuru cheZeroOps ndechekubvisa kudiwa kwekupindira kwemaoko mukuendesa uye manejimendi emasoftware masisitimu, nekudaro kuderedza mukana wekukanganisa kwevanhu uye kuwedzera kukurumidza uye kushanda kwesoftware kuendesa.

AI Agents uye Context Engineering

AI vamiririri uye mamiriro einjiniya anoita basa rakakosha mukuitwa kweZeroOps. AI vamiririri mapurogiramu esoftware anogona kuona nharaunda yavo uye kutora matanho kuti azadzise zvinangwa chaizvo. Context engineering, kune rumwe rutivi, inosanganisira kugadzira nekuita masisitimu anogona kunzwisisa uye kupindura kune mamiriro aanoshanda.

Muchirevo cheZeroOps, vamiririri veAI vanogona kushandiswa kuita otomatiki mabasa akasiyana siyana sekuendesa, kuyedzwa, uye kutarisa. Context engineering inogona kushandiswa kugadzira masisitimu anogona kunzwisisa mamiriro mavari kushanda, senge nharaunda, mushandisi, uye basa riripo.

StackGen: Nzira-Yakavakirwa Pateni yeAI DevOps otomatiki

StackGen ndeye yakavhurika-sosi sisitimu inogonesa vanogadzira kuita nzira-yakavakirwa maitiro kuAI DevOps otomatiki. StackGen inopa seti yezvakafanotsanangurwa mapatani uye matemplate anogona kushandiswa kuita otomatiki mabasa akasiyana siyana sekuendesa, kuyedzwa, uye kutarisa.

StackGen yakavakirwa pamusoro peLangChain, dhizaini inogonesa vanogadzira kuvaka yekukurukurirana AI masisitimu. LangChain inopa seti ye APIs uye zvishandiso zvinogona kushandiswa kuvaka kutaurirana AI masisitimu anogona kunzwisisa uye kupindura kune echisikigo mitauro yemutauro.

**LangChain: Kuvaka Kukurukurirana AI Systems **

LangChain chimiro chinogonesa vanogadzira kuvaka yekukurukurirana AI masisitimu. LangChain inopa seti ye APIs uye zvishandiso zvinogona kushandiswa kuvaka kutaurirana AI masisitimu anogona kunzwisisa uye kupindura kune echisikigo mitauro yemutauro.

LangChain yakavakirwa pamusoro pehuwandu hweakavhurika-sosi matekinoroji, anosanganisira TensorFlow uye PyTorch. LangChain inopa seti ye APIs uye zvishandiso zvinogona kushandiswa kuvaka kutaurirana AI masisitimu anogona kunzwisisa uye kupindura kune echisikigo mitauro yemutauro.

Kuita ZeroOps neStackGen uye LangChain

Kuti tiite ZeroOps neStackGen uye LangChain, tinoda kutevedzera akati wandei matanho:

Tsanangura AI Agent: Danho rekutanga pakushandisa ZeroOps nderekutsanangura mumiririri weAI achashandiswa kuita mabasa akasiyana siyana. Iyo AI mumiriri inogona kutsanangurwa uchishandisa akati wandei ekare-akatsanangurwa mapatani uye matemplate anopihwa neStackGen.
** Gadzira Injini Yechinyorwa **: Nhanho inotevera ndeyekugadzira injini yemamiriro ezvinhu iyo ichashandiswa kunzwisisa uye kupindura kune mamiriro ekushandiswa kweAI agent. Injini yemamiriro ezvinhu inogona kugadzirwa uchishandisa nhamba yeAPIs uye zvishandiso zvakapiwa naLangChain.
** Shandisa iyo AI Agent **: Kana iyo AI agent uye injini yemamiriro ezvinhu yakatsanangurwa, danho rinotevera nderokushandisa AI agent achishandisa nhamba ye APIs uye zvishandiso zvakapiwa naStackGen uye LangChain.
** Kuedza uye Kushandisa **: Nhanho yekupedzisira ndeyekuedza uye kutumira mumiririri weAI munzvimbo yekugadzira.

Muenzaniso kodhi: Kuita Nyore AI Agent uchishandisa StackGen uye LangChain

Heino muenzaniso kodhi block inoratidza maitiro ekushandisa ari nyore AI mumiriri uchishandisa StackGen uye LangChain:

import langchain
kubva langchain.chains import LLMChain
kubva ku langchain.llms import LLM


ai_agent = LLMChain(
    llm=LLM(
        model_name = "davinci:3-batch",
        tembiricha=0.5,
        max_tokens=100,
    ),
    input_format="zvinyorwa",
    output_format = "chinyorwa",
)

# Tsanangura injini yemukati
context_engine = langchain.ContextEngine(
    llm=LLM(
        model_name = "davinci:3-batch",
        tembiricha=0.5,
        max_tokens=100,
    ),
    input_format="zvinyorwa",
    output_format = "chinyorwa",
)

# Shandisa iyo AI mumiriri
ai_agent.implement(
    context_engine=context_injini,
    input_text="Hello, makadii?",
)

# Edza uye tumira iyo AI mumiriri
ai_agent.test_and_deploy(
    input_text="Hello, makadii?",
)

Iyi kodhi yekodhi inoratidza maitiro ekushandisa ari nyore AI mumiriri uchishandisa StackGen uye LangChain. Iyo AI mumiriri inotsanangurwa uchishandisa akati wandei ekare-akatsanangurwa mapatani uye matemplate akapihwa neStackGen. Injini yemamiriro ezvinhu yakagadzirirwa kushandisa nhamba yeAPIs uye zvishandiso zvakapiwa naLangChain. Iyo AI mumiriri inoshandiswa uchishandisa akati wandei maAPI uye zvishandiso zvinopihwa neStackGen uye LangChain. Pakupedzisira, mumiriri weAI anoedzwa uye anoiswa munzvimbo yekugadzira.

Pamusoro 5-10 Inonyanya Kushandiswa Mirairo/Zvidimbu

Heano epamusoro 5-10 anonyanya kushandiswa mirairo/zvimedu zveStackGen uye LangChain:

langchain.LLMChain(): Inogadzira chinhu chitsva cheLLM cheni.
langchain.ContextEngine(): Inogadzira chinhu chitsva cheinjini.
LLM(model_name="davinci:3-batch", tembiricha=0.5, max_tokens=100): Vanogadzira chinhu chitsva cheLLM chine zita remuenzaniso, tembiricha, uye ma tokens makuru.
ai_agent.implement(context_engine=context_engine, input_text="Hello, makadii?"): Inotevedzera mumiririri weAI uchishandisa injini yemamiriro ekunze uye mavara ekuisa.
ai_agent.test_and_deploy(input_text="Hello, makadii?"): Yedza uye inoshandisa mumiririri weAI uchishandisa mavara akataurwa.
langchain.llms.LLM(): Inogadzira chinhu chitsva cheLLM.
langchain.chains.LLMChain(): Inogadzira chinhu chitsva cheLLM cheni.
langchain.ContextEngine(llm=LLM(model_name="davinci:3-batch", tembiricha=0.5, max_tokens=100)): Inogadzira chinhu chitsva cheinjini uchishandisa chinhu chakataurwa cheLLM.
ai_agent.context_engine = langchain.ContextEngine(llm=LLM(model_name="davinci:3-batch", temperature=0.5, max_tokens=100)): Inoisa injini yemamiriro emumiririri weAI kuchinhu chakataurwa chejini.
ai_agent.input_text = "Mhoro, makadii?": Inogadzirisa zvinyorwa zvemumiririri weAI kuti zvienderane nezvakanyorwa.

Kuona

Heino tarisiro yeZeroOps architecture uchishandisa Chart.js:

{
  "type": "bha",
  "data": {
    "labels": ["AI Agent", "Context Engine", "StackGen", "LangChain"],
    "datasets": [
      {
        "label": "Architecture",
        "data": [10, 8, 6, 4],
        "backgroundColor": ["#FF6384", "#36A2EB", "#FFCE56", "#4BC0C0"]
      }
    ]
  },
  "sarudzo": {
    "zita": {
      "kuratidza": chokwadi,
      "zvinyorwa": "ZeroOps Architecture"
    },
    "legend": {
      "kuratidza": chokwadi
    },
    "zvikero": {
      "yAxes": [
        {
          "zvikwekwe": {
            "beginAtZero": chokwadi
          }
        }
      ]
    }
  }
}

Ichi chitarisiko chinoratidza mavakirwo eZeroOps system, kusanganisira iyo AI mumiriri, injini yemamiriro, StackGen, uye LangChain.

Mhedziso

ZeroOps inzira yechimurenga kuDevOps otomatiki inotsvaga kubvisa kudiwa kwekupindira kwemanyore mukuendesa uye manejimendi esoftware masisitimu. AI vamiririri uye mamiriro einjiniya anoita basa rakakosha mukuitwa kweZeroOps, uye StackGen neLangChain vanopa maitiro-akavakirwa maitiro kuAI DevOps otomatiki. Nekushandisa iri nyore AI mumiriri uchishandisa StackGen uye LangChain, tinogona otomatiki mabasa akasiyana siyana sekuendesa, kuyedzwa, uye kutarisa. Iyo yepamusoro 5-10 inonyanya kushandiswa mirairo / zvimedu zveStackGen uye LangChain zvinopihwa zvakare, pamwe nekuona kweZeroOps architecture uchishandisa Chart.js.

Kuvhura ZeroOps neAI Agents uye Context Engineering: Iyo Pateni-Yakavakirwa Nzira yeAI DevOps Automation uchishandisa StackGen uye LangChain.

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