Okwu mmalite
Mgbanwe nke DevOps agbanweela ụzọ e si emepụta ngwanro, ibunye na jikwaa ya. Otú ọ dị, ka mgbagwoju anya nke usoro ngwanrọ ọgbara ọhụrụ na-eto eto, otu ahụ ka ọ dị mkpa maka akpaaka, nrubeisi, na scalability. Nke a bụ ebe DevOps na-akwado AI na-abata - usoro mgbanwe egwuregwu nke na-eme ka ọgụgụ isi (AI) na mmụta igwe (ML) kwalite pipeline DevOps. N'edemede a, anyị ga-enyocha ka esi hazie DevOps nke AI na-akwado ya na LangChain na DSPy, usoro mbelata ọnụ abụọ na-eme ka akpaaka dabere na onye nnọchi anya.
Kedu ihe bụ LangChain?
LangChain bụ usoro mepere emepe nke na-enye ndị mmepe ohere iwulite ngwa mkparịta ụka AI site na iji ihe owuwu modular, nke nwere ike itolite. Ọ na-enye usoro ihe eji arụ ọrụ na ngwa ọrụ maka ịmepụta ndị ọrụ AI nke nwere ike ịmekọrịta na isi mmalite data dị iche iche, rụọ ọrụ, ma mee mkpebi. Ewubere LangChain n'elu asụsụ mmemme Rust ma enwere ike iji ya na asụsụ ndị ọzọ dị ka Python na Go.
Gịnị bụ DSPy?
DSPy bụ ọba akwụkwọ Python nke na-enye ngwa ngwa Pythonic na usoro LangChain. Ọ na-enye ndị mmepe ohere iwu ndị ọrụ AI site na iji syntax maara nke ọma na usoro ọrụ, na-eme ka ọ dịkwuo mfe ijikọ LangChain na pipeline DevOps dabeere na Python.
Na-ejikọta LangChain na DSPy na pipeline DevOps
Iji jikọta LangChain na DSPy na pipeline DevOps, anyị kwesịrị ịmepụta onye ọrụ AI nke nwere ike ịrụ ọrụ, mee mkpebi, na ịmekọrịta na isi mmalite data dị iche iche. Nke a bụ ntụle ọkwa dị elu nke usoro a:
- ** Kọwaa AI Agent ***: Mepụta onye ọrụ LangChain nke nwere ike ịrụ ọrụ, dị ka ntinye koodu, ule na-agba ọsọ, na nlekota oru.
- ** Jikota na Ngwa DevOps ***: Jikọta onye ọrụ AI na ngwaọrụ DevOps, dị ka Jenkins, GitLab CI / CD, ma ọ bụ Docker.
- ** Hazie AI Agent ***: Hazie onye ọrụ AI ka ọ na-emekọrịta ihe na isi mmalite data dị iche iche, dị ka ọdụ data, API, ma ọ bụ sistemụ faịlụ.
- ** Zụlite AI Agent ***: zụọ onye ọrụ AI site na iji igwe mmụta algọridim na data sitere na pipeline DevOps.
Ihe Nlereanya Bara Uru: Koodu ntinye na LangChain na DSPy
Nke a bụ ihe atụ dị mfe nke otu esi etinye koodu site na iji LangChain na DSPy: "Python mbubata langchain si langchain.agents mbubata Agent si langchain.llms bubata LLM
Mepụta onye nnọchi anya LangChain
onye nnọchi anya = onye nnọchi anya ( llm=LLM( model_name = "davinci", max_tokens=2048, okpomọkụ = 0.5, ), yinye =[ langchain. Chain( aha = "deploy_code", nzọụkwụ=[ langchain.Nzọụkwụ( aha = "git_clone", func=lambda: git_clone ("https://github.com/user/repo.git"), ), langchain.Nzọụkwụ( aha = "build_code", func=lambda: build_code(), ), langchain.Nzọụkwụ( aha = "deploy_code", func=lambda: deploy_code(), ), ], ), ], )
Nyefee koodu site na iji onye nnọchi anya AI
agent.deploy_code() `` N'ihe atụ a, anyị mepụtara onye nnọchi anya LangChain nke na-eme usoro atọ:
- Na-eji Git mechie ebe nchekwa koodu.
- Na-ewu koodu site na iji ngwá ọrụ ụlọ.
- Na-etinye koodu ahụ site na iji ngwá ọrụ ntinye.
Onye nnọchi anya AI na-eji ụdị asụsụ (LLM) mee mkpebi na ịrụ ọrụ.
Top 5-10 Iwu/Snippets kacha eji
Nke a bụ ụfọdụ iwu na snippets ndị a na-ejikarị maka LangChain na DSPy:
- ** Mepụta onye nnọchi anya LangChain **: `agent = Agent (llm = LLM (model_name = "davinci", max_tokens = 2048, okpomọkụ = 0.5), yinye = [langchain.Chain (aha = "deploy_code", nzọụkwụ = [langchain.Step (aha = "git_clone",): func = lamb git_clone("https://github.com/user/repo.git"))]))'
- ** Nyefee koodu site na iji onye nnọchi anya AI **:
agent.deploy_code() - ** Zụlite onye nnọchi anya AI **: `agent.train(data=[{"ntinye": "koodu ebuga", "mmepụta": "koodu ebugara"}])'
- ** Tinye na ngwaọrụ DevOps **:
agent.integrate_with_jenkins() - ** Hazie onye ọrụ AI **:
agent.configure(llm=LLM(model_name="davinci",max_tokens=2048, okpomọkụ =0.5)) - ** Nweta ọkwa onye nnọchite AI **:
agent.status() - ** Nweta ndekọ onye nnọchi anya AI **:
agent.logs() - ** Melite nhazi onye ọrụ AI **:
agent.update_config(llm=LLM(model_name="davinci", max_tokens=2048, okpomọkụ=0.5)) - ** Hichapụ onye nnọchi anya AI **:
agent.delete() - ** Nweta metrics nke onye nnọchite AI **:
agent.metrics()
Nlere anya
Nke a bụ nleba anya nke ụlọ ọrụ LangChain na DSPy na-eji Chart.js: Json { "ụdị": "ogwe", "data": { "labels": ["LangChain", "DSPy"], "datasets": [ { "label": "Components", "data": [10, 5], "backgroundColor": ["#4CAF50", "#FF9800"], "BorderColor": ["#3e8e41", "#ff9900"], "BorderWiidth": 1 } ] }, "Nhọrọ": { " aha": { "ngosi": eziokwu, "ederede": "LangChain na DSPy Architecture" }, "akụkọ": { "ngosi": eziokwu }, "ọnụego": { "yAxes": [ { "akara aka": { "beginAtZero": eziokwu } } ] } } } `` Ọhụụ a na-egosi ọnụọgụ nke akụrụngwa na LangChain na DSPy.
Mmechi
N'edemede a, anyị nyochara otu esi ahazi DevOps nke AI na-arụ ọrụ na LangChain na DSPy, nhazi ihu abụọ na-eme ka akpaaka dabere na onye nnọchi anya. Anyị tụlere uru nke DevOps na-akwado AI, gụnyere akpaaka emelitere, nrube isi, na scalability. Anyị nyekwara ihe atụ bara uru nke itinye koodu site na iji LangChain na DSPy, yana ndepụta nke iwu na snippets ndị a na-ejikarị eme ihe. N'ikpeazụ, anyị na-eji Chart.js hụ anya LangChain na DSPy architecture. Site na LangChain na DSPy, ndị mmepe nwere ike iwulite pipeline DevOps na-arụ ọrụ nke ọma, na-agbanwe agbanwe na akpaghị aka nke na-enye ike AI na mmụta igwe.