** ZeroOps: Inyefe Agentic AI maka ibunye ngwanrọ akpaaka yana imeziwanye DevOps ***

** Okwu mmalite**

N'ọdịdị mmepe ngwanrọ na-agba ọsọ taa, mkpa maka usoro mbugharị nke ọma na akpaaka adịbeghị mkpa karịa. Ụzọ ọdịnala na DevOps, nke na-adabere na ntinye aka na ntinye akwụkwọ, ezughịkwa ezu iji gboo ihe achọrọ nke mmepe ngwanrọ ọgbara ọhụrụ. Nke a bụ ebe ZeroOps na-abata - usoro mgbanwe nke na-eme ka AI na-arụ ọrụ na-arụ ọrụ na-arụ ọrụ na-arụ ọrụ na ngwanrọ DevOps.

N'isiokwu a, anyị ga-enyocha njikọ nke ndị ọrụ AI, nleba anya nke ọgụgụ isi, na automation na DevOps modernization, na-elekwasị anya na mmejuputa ụkpụrụ ZeroOps site na iji LangChain na DSPy. Anyị ga-enyocha akụkụ bara uru nke mmejuputa ZeroOps, gụnyere ọmụmaatụ koodu na ndepụta iwu, yana wepụta nhụta uru nke ụzọ a.

Gịnị bụ ZeroOps?

ZeroOps bụ paradaịs nke na-achọ iwepụ mkpa ntinye aka na ntinye ngwanrọ na usoro DevOps. Site n'ịkwalite ndị nnọchi anya AI, ZeroOps na-enyere aka ime mkpebi akpaaka, nleba anya na-aga n'ihu, na njikarịcha ngwa ngwa ngwa ngwa pipeline. Emebere usoro a iji belata mgbagwoju anya na ihe egwu jikọtara ya na omume DevOps ọdịnala, ebe ọ na-emeziwanye ọsọ na ntụkwasị obi nke ibunye ngwanrọ.

** Ọrụ AI Agents na ZeroOps ***

Ndị nnọchi anya AI na-arụ ọrụ dị oke mkpa na ZeroOps site n'inye nleba anya na ikike akpaaka. Ndị ọrụ ndị a nwere ike nyochaa data sitere na isi mmalite dị iche iche, gụnyere ndekọ ngwa, metrik arụmọrụ, na nzaghachi onye ọrụ, iji chọpụta ụkpụrụ na adịghị mma. Dabere na nyocha a, onye na-ahụ maka AI nwere ike ịme mkpebi ziri ezi gbasara ibunye ngwanrọ, dị ka ikpebi atụmatụ mbugharị kacha mma, ịchọpụta ihe nwere ike ime, yana ịkwado ihe ndozi.

** LangChain na DSPy: Na-enyere ZeroOps aka ***

LangChain na DSPy bụ usoro abụọ a ma ama nke na-eme ka mmepe nke ndị ọrụ AI kwadoro maka ZeroOps. LangChain bụ ọbá akwụkwọ Python nke na-enye usoro ngwaọrụ maka iwulite ndị ọrụ AI mkparịta ụka, ebe DSPy bụ usoro nchara nke na-enyere aka mmepe nke ndị ọrụ AI na-eduzi data.

N'isiokwu a, anyị ga-elekwasị anya na iji LangChain na DSPy mejuputa ụkpụrụ ZeroOps. Anyị ga-enye ọmụmaatụ koodu na ndepụta iwu iji gosi akụkụ bara uru nke imejuputa ZeroOps site na iji usoro ndị a.

** Iji LangChain na DSPy na-eme ZeroOps ***

Iji mejuputa ZeroOps site na iji LangChain na DSPy, anyị ga-agbaso usoro nzọụkwụ site na nzọụkwụ:

1. ** Nchịkọta data ***: Anakọta data sitere na isi mmalite dị iche iche, gụnyere ndekọ ngwa, metrik arụmọrụ, na nzaghachi onye ọrụ.
2. ** Nyocha data ***: Nyochaa data anakọtara site na iji ndị na-akwado AI iji chọpụta usoro na ihe adịghị mma.
3. **Mkpebi-Mkpebi ***: Jiri nghọta enwetara site na nyocha data mee mkpebi ziri ezi gbasara ibunye ngwanrọ.
4. ** Automation ***: Na-ebugharị ngwanro akpaaka dabere na mkpebi nke onye nnọchi anya AI mere.

Nke a bụ ihe atụ koodu dị mfe iji LangChain na DSPy iji mejuputa ZeroOps: "Python mbubata langchain sitere na onye nnọchi anya mbubata dspy

onye nnọchi anya = onye nnọchi anya ( aha = "ZeroOps Agent", nlereanya = "langchain/llama:1.3B", ozugbo = "Tinye ngwanrọ na gburugburu mmepụta", )

Chịkọta data sitere na isi mmalite dị iche iche

data = { "application_logs": ["log1", "log2", "log3"], "performance_metrics": {"cpu": 80, "ncheta": 90}, "user_feedback": ["positive", "negative", "neutral"], }

Nyochaa data anakọtara

analysis = agent.analyze(data)

Mee mkpebi ziri ezi gbasara ibunye ngwanrọ

mkpebi = agent.ikpebi(analysis)

Mepụta ngwanro ngwa ngwa

deployment = agent.deploy(mkpebi) `` Ihe atụ koodu a na-egosi otu esi eji LangChain na DSPy mejuputa ụkpụrụ ZeroOps. Onye na-ahụ maka AI na-anakọta data site na isi mmalite dị iche iche, nyochaa data, na-eme mkpebi ziri ezi gbasara ntinye ngwanrọ, ma na-emezi usoro mbugharị.

** Iwu 5-10 Kachasị Eji mee ihe

Nke a bụ iwu/snippets 5-10 kacha eji emejuputa ZeroOps iji LangChain na DSPy:

1. `langchain.Agent (aha = " ZeroOps Agent ", model = "langchain / llama: 1.3B", ozugbo = "Tinye ngwanrọ na gburugburu mmepụta")'
2. `dspy.Agent (aha = " ZeroOps Agent ", model = "langchain / llama: 1.3B", ozugbo = "Tinye ngwanrọ na gburugburu ebe mmepụta")'
3. data = {"application_logs": ["log1", "log2", "log3"], "performance_metrics": {"cpu": 80, "memory": 90}, "user_feedback": ["ezigbo", "nagative", "anọpụ iche"]}
4. Analysis = agent.nyocha(data)
5. mkpebi = agent.ikpebi(analysis)
6. deployment = agent.deploy(mkpebi)
7. `langchain.llama.LlamaModel.load("langchain/llama:1.3B")'
8. `dspy.Agent.load("langchain/llama:1.3B")'
9. agent.train(data)
10. agent.save("zeroops_agent.pkl")

** Nlegharị anya ***

Nke a bụ nleba anya nke uru ZeroOps na-eji Chart.js: Json { "ụdị": "ogwe", "data": { "labels": ["Njikwa akwụkwọ ntuziaka", "ZeroOps"], "datasets": [ { "label": "Oge Nkwanye", "data": [10, 2], "backgroundColor": ["rgba(255, 99, 132, 0.2)", "rgba (54, 162, 235, 0.2)"], "BorderColor": ["rgba (255, 99, 132, 1)", "rgba (54, 162, 235, 1)"], "BorderWiidth": 1 }, { "label": "Ụdị mperi", "data": [5, 1], "backgroundColor": ["rgba(255, 99, 132, 0.2)", "rgba (54, 162, 235, 0.2)"], "BorderColor": ["rgba (255, 99, 132, 1)", "rgba (54, 162, 235, 1)"], "BorderWiidth": 1 } ] }, "Nhọrọ": { " aha": { "ngosi": eziokwu, "text": "Uru nke ZeroOps" }, "ọnụego": { "yAxes": [ { "ngosi": eziokwu, "scaleLabel": { "ngosi": eziokwu, "labelString": "Uru" } } ] } } } `` Ọhụụ a na-egosipụta uru nke ZeroOps, gụnyere mbelata oge ntinye na ọnụego mperi.

Mkpebi

ZeroOps bụ usoro mgbanwe maka imeziwanye DevOps nke na-eme ka AI na-arụ ọrụ na-arụ ọrụ iji rụọ ọrụ ntinye ngwanrọ na ntinye / nnyefe na-aga n'ihu (CI/CD). Site n'iji LangChain na DSPy, ndị mmepe nwere ike mejuputa ụkpụrụ ZeroOps ma nweta uru dị ịrịba ama, gụnyere mbelata oge ntinye na ọnụego njehie. Site n'ịbawanye ụbara maka ibunye ngwanrọ na-arụ ọrụ nke ọma na akpaaka, ZeroOps dị njikere ịghọ onye ọkpụkpọ isi na mpaghara DevOps.