Funda Ukuklama Amasistimu e-AI Isinyathelo Ngesinyathelo: Kusukela ku-LLM Inference kuya Kuma-Agent Akhiqizayo

Uyini Umsebenzi Wokuklama Uhlelo lwe-AI?

Ukuklama Uhlelo lwe-AI umhlahlandlela ovulekile wokufunda ogcinwa ngu-Amit Shekhar, umsunguli we-Outcome School. Uchaza indlela yokuklama lonke uhlelo oluzungeze imodeli yolimi enkulu, kunokuphatha umkhiqizo we-AI njengocingo olulodwa lwe-API yemodeli.

Lo mhlahlandlela uhlanganisa izingxenye ezijwayelekile zokuklama izinhlelo, njengamabhange edatha, ama-cache, ama-queue, ama-load balancer, nama-gateway, nezithiyo eziqondene ne-AI, njengama-GPU, ama-token, izicelo ezithatha isikhathi eside, okukhiphayo okusakazwayo, ukuziphatha okunganqunywa kusengaphambili, kanye nezindleko zemodeli ngesicelo ngasinye.

Ukuklama Uhlelo lwe-AI kuwukuklama uhlelo okuvamile kanye nezithiyo ezintsha zokusebenza ezethulwa amamodeli e-AI.

Lokhu okuqukethwe kuhloselwe onjiniyela be-AI, onjiniyela be-LLM, onjiniyela be-backend, onjiniyela bokufunda ngomshini, onjiniyela bezinkundla, abaklami bezinhlelo, abafundi, kanye nanoma ubani olungiselela inhlolokhono yokuklama uhlelo lwe-AI.

Kungani Ukuklama Uhlelo lwe-AI Kubalulekile

Ukubiza i-API ye-LLM kuskripthi kungadinga imigqa embalwa kuphela yekhodi. Ukusebenzisa leso sici kubasebenzisi abaningi kuyinkinga ehlukile. Izinhlelo ezisetshenziswa ekukhiqizeni kufanele zilawule ukubambezeleka, umthamo wokucubungula, ukwethembeka, ubumfihlo, ikhwalithi yokuphumayo, nezindleko, kuyilapho ziphatha ama-prompt amade nezimpendulo eziza nge-token ngayinye.

Isibonelo, umsizi wokweseka amakhasimende osebenza ekukhiqizeni angase adinge uku:

• Aqale ukuphendula ngokushesha, ngisho nalapho ithrafikhi inyuka.
• Asekele izimpendulo kumadokhumenti enkampani ngokusebenzisa i-retrieval.
• Avimbele ukuvuza kolwazi oluyimfihlo.
• Alawule ukwehluleka kwamamodeli, ama-timeout, nemikhawulo yezinga.
• Akalise ikhwalithi yezimpendulo naphezu kokuphumayo okunganqunywa kusengaphambili.
• Ahlale ngaphakathi kwesabelomali esichaziwe sama-token nengqalasizinda.

Indawo yokugcina ikhodi ihlela izingxenye zokwakha ezidingekayo ukuxazulula lezi zinkinga futhi ixhumanisa izihloko eziningi nezindatshana ezichaza kabanzi.

Izihloko Ezisemqoka Nezici

Izisekelo ze-inference

Umhlahlandlela uqala ngama-token, amaseva e-inference, izinjini ze-inference, kanye nehadiwe ye-AI efana nama-GPU, ama-TPU, nama-LPU. Uchaza ukuthi i-LLM ikhiqiza umbhalo ngokulandelana, ibikezele i-token eyodwa bese isebenzisa lokho okukhiphayo lapho ibikezela i-token elandelayo.

Uphinde wethule izigaba ezimbili ezinkulu ze-inference:

• Prefill: ukucubungula i-prompt yokufaka nokwakha isimo sangaphakathi esidingekayo ekukhiqizeni.
• Decode: ukukhiqiza impendulo i-token eyodwa ngesikhathi.

Lezi zigaba zithinta isikhathi sokuthola i-token yokuqala, inani lama-token ngomzuzwana, umthamo wokucubungula, kanye nezinqumo zengqalasizinda. Umhlahlandlela uphinde ukhulume nge-chunked prefill nokuhlukanisa imithwalo ye-prefill ne-decode.

Ukwandisa umthamo, ukuqondisa, nokugcina i-cache

Izigaba zokwandisa umthamo zihlanganisa i-vertical scaling, i-horizontal scaling, i-tensor parallelism, i-pipeline parallelism, i-autoscaling, ukulinganisa ama-GPU, nokulinganisa izindleko. Izindlela zokulinganisa umthwalo eziqondene nama-LLM zihlanganisa ama-token asalindile ambalwa, ukuqondisa okwazi ama-prefix, kanye nama-sticky session.

Okuqukethwe mayelana nama-cache kuhlukanisa izingqimba eziningana:

• KV cache yokusebenzisa kabusha okhiye namanani e-attention ngesikhathi sokukhiqiza.
• Prompt cache yama-prefix e-prompt aphindaphindwayo.
• Semantic cache yemibuzo enencazelo efanayo.
• Embedding cache yokugwema ukubalwa okuphindaphindiwe kwama-embedding.

Ukuqondiswa kwamamodeli nakho kuyachazwa, okuhlanganisa izindlela ezisekelwe emithethweni, kuma-classifier, kuma-embedding, kuma-LLM, kanye nama-cascade.

Ukusesha nokuphathwa komongo

Umhlahlandlela uchaza ama-embedding, amabhange edatha ama-vector, ama-vector index, kanye ne-retrieval-augmented generation, evame ukubizwa nge-RAG. Indlela yawo yokusesha ihlanganisa ukuhlukanisa amadokhumenti, ukufaka idatha, ukuhlukanisa izingcezu, ukusesha okuxubile, ukuguqulwa kombuzo kwe-HyDE, ukuhlela kabusha, i-ColBERT, i-Agentic RAG, i-GraphRAG, kanye ne-vectorless RAG.

Uphinde ubhekane nemikhawulo yewindi lomongo ngokusebenzisa ukusika, ukufingqa, amawindi ashelelayo, ukucindezela, kanye nenkumbulo ehlelekile. Imiqondo ehlobene ihlanganisa ukubola komongo, ukuziphatha kwe-lost-in-the-middle, ukubola kwe-RoPE, kanye nobunjiniyela bomongo.

Ama-agent nezinhlelo zama-agent amaningi

Izigaba zama-agent zihlola izingxenye eziyinhloko ze-agent ye-AI, izihibe zama-agent kusukela ekuqaleni kuye ekugcineni, ukubiza amathuluzi, okukhiphayo okuhlelekile, inkumbulo, kanye nezindlela ezivamile zokwehluleka. Ziphinde zihlukanise ukuhlelwa kwe-AI okunqunywe kusengaphambili nokuziphatha kwe-agent okuzimele kakhulu.

Ezinye izihloko zihlanganisa i-Model Context Protocol, noma i-MCP, ama-Agent Skills, ama-agent asebenzisa amakhompyutha, ama-browser agent, ubunjiniyela bezihibe, ubunjiniyela bamagrafu, ama-subagent, izindlela zokuxhumanisa, kanye ne-Agent2Agent protocol.

Ukusebenza kokukhiqiza

Indawo yokugcina ikhodi idlulela ngalé kwezingcingo zamamodeli iye ezindabeni zokukhiqiza ezifana nalezi:

• Ukusakaza nge-Server-Sent Events noma ama-WebSocket.
• Ukucubungula okungavumelani kanye nama-message queue emisebenzini emide.
• Imikhawulo yezinga esekelwe ezicelweni, kuma-token, ekusebenzeni kanyekanye, noma ezindlekweni.
• Ama-AI gateway nezakhiwo ezinezinqashi eziningi.
• Ama-guardrail, izivikelo ekujovweni kwe-prompt, kanye ne-AI red teaming.
• Ukusulwa kwe-PII, indawo yokuhlala kwedatha, ama-audit log, kanye nemigomo yokuthobela imithetho.
• Ukulandelela, ukubonakala kokusebenza, ukuhlola, kanye nezinqubo ze-LLM-as-a-judge.
• Ama-timeout, ama-retry ane-backoff, amamodeli ayisipele, kanye nokwehlisa ukusebenza ngendlela elawulwayo.

Uphinde uhlanganise ukuthuthukiswa kwe-inference okufana ne-quantization, i-continuous batching, i-speculative decoding, i-Flash Attention, i-Mixture of Experts, i-model distillation, kanye ne-Grouped Query Attention.

Ukufakwa Nokulungiselela

Le ndawo yokugcina ikhodi ingumhlahlandlela wokufunda kunokuba ibe umtapo wolwazi wesoftware, ngakho ayidingi ukufakwa kwamaphakheji, ukhiye we-API, noma indawo yokusebenza. Ungayifunda ngokuqondile ku-GitHub.

Ukuze ugcine ikhophi yasendaweni, clone-a indawo yokugcina ikhodi nge-Git:

git clone https://github.com/amitshekhariitbhu/ai-system-design.git
cd ai-system-design

Vula i-README kusibukeli se-Markdown noma kumhleli okwazi ukubonisa i-Markdown. Njengoba lo mhlahlandlela uqhubeka unwetshwa, buyekeza ikhophi yakho yasendaweni ngezikhathi ezithile:

git pull

Ayikho imiyalelo yokwakha echazwe ku-README. Umsebenzi oyinhloko wokusetha ukukhetha indlela yokufunda nokusebenzisa uhlu lokuqukethwe ukuzulazula phakathi kwemiqondo.

Indlela Yokusebenzisa Lo Mhlahlandlela

Inketho 1: Landela yonke indlela yokufunda

Uma umusha ekwakhiweni kwezinhlelo ze-AI, funda umhlahlandlela ngokulandelana. Ukuqhubeka kwemiqondo okunconyiwe yilokhu:

1. Funda ngamathokheni, amaseva e-inference, kanye nezingxenyekazi zekhompuyutha ze-AI.
2. Qonda i-prefill, i-decode, ukubambezeleka, kanye nokuphuma kwemisebenzi.
3. Funda ngokwandisa isikali, ukulinganisa, kanye nokulinganisa umthwalo.
4. Qhathanisa amasu okugcina idatha okwesikhashana kanye nawokukhetha umzila wamamodeli.
5. Funda ngama-embeddings, amabhange edatha ama-vector, kanye ne-RAG.
6. Funda ngokuphathwa kwewindi lomongo.
7. Hlola i-streaming, olayini, ukukhawulelwa kwezinga, kanye nama-gateway.
8. Funda ngama-agent, amathuluzi, i-MCP, okukhiphayo okuhlelekile, kanye nenkumbulo.
9. Qhubekela ezinhlelweni ezinama-agent abaningi, ezisebenzisa izinhlobo eziningi zemidiya, kanye nezinhlelo zezwi.
10. Engeza izivikelo, ubumfihlo, ukubonakala kokusebenza, kanye nokuhlola.
11. Funda ngokulawula izindleko, ingqalasizinda yokulungisa kahle amamodeli, kanye nokuthuthukisa i-inference.
12. Sebenzisa uhlaka lokuxazulula izinkinga zokuklama izinhlelo olusetshenziswa yile ndawo yokugcina ikhodi.

Inketho 2: Lungiselela inhlolokhono

Ukuze ulungiselele inhlolokhono, qala ngesigaba esikhuluma ngokuxazulula noma iyiphi inkinga yokuklama uhlelo lwe-AI. Bese ubuyela ezingxenyeni eziyisisekelo zobuchwepheshe ezidingekayo esinqumweni ngasinye sokuklama. Uhlaka lulandela izigaba eziyisishiyagalombili:

1. Izidingo: khomba abasebenzisi, isikali, imigomo yokubambezeleka, izidingo zobumfihlo, kanye nemikhawulo.
2. Inhloso ye-AI: chaza lokho imodeli okufanele ikufeze.
3. Ukulungiswa kwedatha: nquma ukuthi idatha yomthombo iqoqwa, ihlaziywa, ihlanzwe, futhi ifakwe kanjani enkombeni.
4. Ukuklama i-architecture: beka ama-gateway, amasevisi, amamodeli, i-retrieval, ama-cache, olayini, kanye nesitoreji.
5. Ukukhetha imodeli kanye nokwenza ama-prompt: khetha amamodeli namasu ama-prompt ngokuya ngekhwalithi, ukubambezeleka, kanye nezindleko.
6. Ukuhlola: chaza ukuthi okukhiphayo nokuziphatha kohlelo kuzokalwa kanjani.
7. Ukuthunyelwa kanye nokusevisa: hlela i-inference, ukwandisa isikali, i-streaming, kanye nokumelana nokwehluleka.
8. Ukuqapha: qapha ikhwalithi, ukubambezeleka, amaphutha, ukusetshenziswa kwamathokheni, kanye nezindleko.

Inketho 3: Yisebenzise njengereferensi

Uma usuvele wakha izinhlelo ze-AI, sebenzisa uhlu lokuqukethwe njengereferensi. Isibonelo, bheka izigaba zokugcina idatha okwesikhashana lapho unciphisa ukubalwa okuphindaphindiwe, izigaba ze-RAG lapho ikhwalithi ye-retrieval iphansi, noma izigaba zokubonakala kokusebenza nokuhlola lapho uhlelo lusebenza ngobuchwepheshe kodwa ikhwalithi yezimpendulo zalo ingacacile.

Isibonelo Sokusetshenziswa Okuyisisekelo: Ukwakha Umsizi Wokweseka

Le ndawo yokugcina ikhodi ayilona uhlaka olusebenzisekayo, ngakho “ukusetshenziswa” kusho ukusebenzisa imiqondo yayo enkingeni yokuklama. Cabanga ngomsizi wokweseka amakhasimende okufanele aphendule imibuzo esebenzisa imibhalo eyimfihlo yenkampani.

1. Chaza izidingo

Bhala phansi izidingo zokusebenza nezokuphatha ngaphambi kokukhetha imodeli:

Indlela yokusetshenziswa: ukuphendula imibuzo yokweseka amakhasimende
Umthombo wolwazi: imibhalo eyimfihlo yomkhiqizo nezinqubomgomo
Indlela yempendulo: umbhalo osakazwayo
Imfuneko yekhwalithi: izimpendulo ezisekelwe emibhalweni etholakele
Imfuneko yokuphepha: akukho ukuvuza kwedatha eyimfihlo
Imigomo yokusebenza: ukubambezeleka okuphansi, izindleko ezilawulwayo, ukumelana nokwehluleka

Faka ithrafikhi elindelekile, ubude be-prompt, ubude bokukhiphayo, inani eliphakeme lezicelo ezisebenza ngesikhathi esisodwa, kanye nokubambezeleka okwamukelekayo lapho izilinganiso zangempela sezitholakala. Lawa manani aqhuba izilinganiso zamathokheni, ama-GPU, ukuphuma kwemisebenzi, kanye nezindleko.

2. Dweba umzila wesicelo

Idizayini esezingeni eliphezulu ehlanganiswe ngezingxenye eziyisisekelo zalo mhlahlandlela ingamelwa kanje:

Ikhasimende
  -> I-AI Gateway
  -> Ukuqinisekisa kanye nokukhawulelwa kwezinga
  -> Izivikelo zokufaka kanye nokuphathwa kwe-PII
  -> Ipayipi le-retrieval
       -> I-embedding yombuzo
       -> Ukusesha kwama-vector noma okuhlanganisiwe
       -> Ukuhlelwa kabusha
  -> Ukwakhiwa kwe-prompt
  -> Umzila wemodeli noma iseva ye-inference
  -> Ukuqinisekiswa kokukhiphayo kanye nezivikelo
  -> Impendulo esakazwayo
  -> Ukulandelela, ukuhlola, kanye nokuqapha izindleko

Leli ishidi lokusebenzela lokuklama, hhayi ikhodi enikezwa yile ndawo yokugcina ikhodi. Ingxenye ngayinye ihambisana nesihloko esihlanganiswe umhlahlandlela.

3. Klama ipayipi le-retrieval

Hlaziya futhi ufake imibhalo yomthombo, uyihlukanise ibe izingcezu ezifanele, wakhe ama-embeddings, bese ugcina ukumelwa okungaseshwa. Ngesikhathi sombuzo, thola izingcezu ezifanele futhi ucabange ngokusesha okuhlanganisiwe noma ukuhlelwa kabusha lapho ukufana kwama-vector kukodwa kunganikezi ukuhambisana okwanele.

Gcina umongo wokugcina ungaphakathi kwewindi lomongo wemodeli. Amasu okuphatha umongo alo mhlahlandlela angasiza lapho okuqukethwe okutholakele, umlando wengxoxo, kanye nemiyalelo kuncintisana ngesikhala.

4. Hlela ukubambezeleka ne-streaming

Linganisa isikhathi sokufika kwethokheni yokuqala ngokwehlukana nesivinini sokukhiqiza. I-prefill ithinta ukuthi ukukhiqiza kungaqala ngokushesha kangakanani, kanti i-decode ithinta ukuthi amathokheni alandelayo afika ngokushesha kangakanani. Ukusakaza nge-SSE noma ama-WebSocket kungathuthukisa ukusabela okubonwa ngumsebenzisi ngaphandle kokunciphisa ukubalwa okuyisisekelo kwemodeli.

5. Engeza ukwethembeka nokuphepha

Sebenzisa ama-timeout, ukuphinda uzame ngokuhlehlisa, amamodeli ayisipele, ukwehlisa ukusebenza ngendlela elawulwayo, kanye nokuqinisekiswa kokukhiphayo. Beka izivikelo zokufaka nezokukhiphayo eduze kwemodeli, vikela ekuhlaselweni kwe-prompt injection, susa i-PII lapho kudingeka, futhi ugcine amarekhodi afanele okuhlola.

6. Hlola futhi ubheke ukusebenza

Landela isicelo sonke kunokuqapha kuphela ucingo lwemodeli. Izilinganiso eziwusizo ezivela kulo mhlahlandlela zihlanganisa ukubambezeleka, ukuphuma kwemisebenzi, ukusetshenziswa kwamathokheni, izindleko, ukuziphatha kwe-retrieval, ikhwalithi yokukhiphayo kwemodeli, ukusetshenziswa kwamathuluzi, kanye nokuziphatha kwe-agent. Ngenxa yokuthi okukhiphayo kwe-LLM akuhlali kufana, ukuhlola kudinga okungaphezu kokuhlolwa okuvamile kwe-unit test okuqhathanisa okufanayo ncamashi.

Amathiphu Athuthukile

Thuthukisa isigaba esifanele sokubambezeleka

Ungaphathi ukubambezeleka kwemodeli njengenombolo eyodwa. Thola ukuthi inkinga iku-prefill, decode, retrieval, queueing, noma ithuluzi langaphandle. Isikhathi sokuthola ithokheni lokuqala kanye nenani lamathokheni ngomzuzwana kumelela ulwazi oluhlukile lomsebenzisi futhi kungadinga ukuthuthukiswa okuhlukile.

Qondisa izicelo esikhundleni sokusebenzisa imodeli eyodwa kuyo yonke into

Umhlahlandlela wethula amasu amaningana okuqondisa amamodeli. Idizayini esebenza ekukhiqizweni ingahlukanisa izicelo bese iqondisa imisebenzi elula iye kumodeli eshibhile, igcine imodeli enamandla kakhulu ezimweni ezinzima. Ukuqondisa nge-cascade kungakhuphula isicelo ezingeni eliphezulu uma impendulo yangaphambilini inganele.

Sebenzisa i-caching kusendlalelo esifanele

Ama-cache e-KV, prompt, semantic, kanye ne-embedding axazulula izinkinga ezahlukene. Qala ngokuthola ukuthi yini ephindaphindwayo: isimo sokukhiqiza, iziqalo zama-prompt, imibuzo enencazelo efanayo, noma ukubalwa kwama-embedding. Bese ukhetha i-cache ehambisanayo futhi ucabange ngokunemba, ubusha, ubumfihlo, kanye nemingcele yabaqashi.

Phatha ikhwalithi ye-retrieval njengepayipi

Ikhwalithi ye-RAG ayincikile kuphela kusizindalwazi sama-vector. Ukuhlukanisa umbhalo, ukwenza ama-chunk, ama-embedding, ukuguqula umbuzo, ukusesha okuhlanganisiwe, ukubeka kabusha izinga, kanye nokuhlanganisa umongo konke kuthinta impendulo yokugcina. Hlola imiphumela ye-retrieval ephakathi ngaphambi kokusola imodeli yolimi.

Sebenzisa ama-agent kuphela lapho ukuhamba komsebenzi kuwadinga

Hlukanisa phakathi kokuhlela okulindelekile kanye ne-agent ekhetha amathuluzi noma izenzo ngamandla. Umhlahlandlela uphawula futhi ukuthi i-MCP ingaba wusizo ekuhlanganiseni okujwayelekile kwamathuluzi nomongo, kodwa ingase ibe yinkimbinkimbi ngokweqile ekuhambeni komsebenzi okulula. Khetha indlela eyinkimbinkimbi kancane ehlangabezana nezidingo.

Linganisa amathokheni, ama-GPU, kanye nezindleko kusenesikhathi

Izilinganiso ezisheshayo ziveza imibono ebizayo ngaphambi kokuqalisa. Linganisa amathokheni okokufaka nawokukhipha ngesicelo ngasinye, umthamo wezicelo, ukusebenza ngesikhathi esisodwa, izidingo zokudlulisa, kanye nezindleko ezilindelekile zemodeli. Khumbula ukuthi umbhalo wesiNgisi ulinganiselwa cishe kuzinhlamvu ezine ngethokheni ngayinye, nakuba ukuhlukaniswa kwamathokheni kwangempela kuncike ku-tokenizer yemodeli.

Chaza isihloko ngasinye ngamazwi akho

I-README incoma ukuchazela omunye umuntu isigaba ngasinye osiqedile. Ukwengeza okuwusizo ukubhala phansi umehluko wezinketho olandela ukukhetha ngakunye kwezakhiwo: kungani ulayini udingeka, kungani i-cache ethile ilusizo, kungani imodeli eyisipele yamukeleka, nokuthi ukukhetha kuthinta kanjani ikhwalithi, ukubambezeleka, ukuphepha, noma izindleko.

Uhlelo Oluphakanyisiwe Lokuzijwayeza

1. Funda isigaba esikhulu esisodwa somhlahlandlela.
2. Fingqa inhloso yaso kanye nokuhlukana kwezinketho ngaphandle kokubheka umbhalo.
3. Faka ingxenye leyo esakhiweni sesibonelo.
4. Chaza okungenani imethrikhi eyodwa yaleyo ngxenye.
5. Chaza indlela eyodwa yokwehluleka kanye nendlela eyodwa yokukunciphisa.
6. Landela isihloko esixhunyiwe esijulile lapho ukubuka konke kunganele.
7. Phinda lo msebenzi ngezifundo zezimo ezifana nabasizi bokubhala amakhodi noma ama-agent ezwi esikhathi sangempela.

Le ndlela iguqula indawo yokugcina amakhodi isuke ohlwini lwezinkomba ibe yikharikhulamu esebenzayo yokuklama.

Isiphetho

I-AI System Design inikeza indlela ebanzi, elungele abaqalayo, esukela ezisekelweni ze-inference ye-LLM iye ezinkingeni zokukhiqiza ezifana ne-RAG, ama-agent, ukuphepha, ukuhlola, ukubonakala, ukubekezelela amaphutha, kanye nokwenza kahle kwezindleko. Akuyona iphakheji okufanele uyifake; ingumhlahlandlela ohlelekile wokuqonda ukuthi izingxenye ze-AI zisebenzisana kanjani.

Qala ngezisekelo uma umusha, noma uqale ngohlaka lokuklama olunezinyathelo eziyisishiyagalombili uma ulungiselela izingxoxo zomsebenzi. Kunoma yikuphi, sebenzisa wonke umqondo ohlelweni oluthile, kala umthelela wawo, bese ubhala phansi umehluko wezinketho olandela izinqumo zakho.