Ngasemva kweearhente ze-AI: Izibonelelo ezixhasa ukuzimela

Uninzi lweenkcazo zeearhente ze-AI kunye neenkqubo ze-arhente zijolise kubuchule bee-arhente ukwenza ngokuzimeleyo, ngaphandle kokungenelela komsebenzisi, kwiimeko ezininzi kwiimeko zokusetyenziswa ezijoliswe kwii-arhente. Ezinye ii-arhente zisebenza ngemodeli yomntu-in-the-loop, zibandakanya umsebenzisi kuphela xa ehlangabezana nokungaqiniseki, kodwa zisebenza ngokuzimeleyo phantsi kweemeko eziqhelekileyo kunye neemeko ezithile.

Ngokuzimela njengophawu oluphambili oluchazayo lweearhente ze-AI, kukho amandla axhasayo afunwa ziiarhente ukuze enze ngokuzimeleyo kwigalelo lomsebenzisi. Kwi iposti yebhlog yangaphambili , sichonge iimfuno ezine ze-arhente ye-AI yoyilo:

1. Ubunakho kunye nokufikelela - Ukukwazi ukwenza egameni lomsebenzisi, kubandakanywa iimvume kunye nokufikelela okuqinisekisiweyo kwiinkqubo ezifanelekileyo.

   
   

2. Ukuqiqa nokucwangcisa - Ukusebenzisa ingqiqo ukwenza izigqibo ngaphakathi kwenkqubo yokucinga ehleliweyo-ehlala ichazwa njengekhonkco, umthi, igrafu, okanye i-algorithm-ekhokela izenzo ze-arhente.

   
   

3. I-orchestration yecandelo - Ulungelelwaniso lwamacandelo amaninzi, kubandakanywa i-prompts, ii-LLMs, imithombo yedatha ekhoyo, umxholo, imemori, imbali, kunye nokuphunyezwa kunye nobume bezenzo ezinokubakho.

   
   

4. I-Guardrails - Iinkqubo zokugcina i-arhente igxininise kwaye isebenzayo, kubandakanywa izikhuselo zokukhusela iimpazamo okanye ukubonelela ngolwazi oluluncedo lokuxilongwa kwimeko yokusilela.

Imfuno nganye kwezi zine ineemfuno ezahlukeneyo zeziseko zophuhliso. Ukufumana amandla kunye nokufikelela, iimfuno eziphambili kukudibanisa isoftware kunye nolawulo lobungqina. Ukuqiqa nokucwangcisa zixhaswa ikakhulu ziiLLMs kunye nezinye iimodeli ze-AI. Isihloko se-guardrails sikhulu kwaye sihlala sithe ngqo kumatyala osetyenziso abandakanyekayo, ke siya kuyigcinela inqaku elizayo. Apha, ndingathanda ukugxila kwiokhestra, kunye neziseko ezingundoqo ezifunekayo ukuxhasa iokhestra ekrelekrele kwinani elikhulu leenxalenye ezihambayo kunye nembali ende yedatha kunye nomxholo onokuthi ufuneke ngexesha lesigqibo.

I-Orchestration yeCandelo kunye nendima yoMxholo kwii-arhente ze-AI

Ukucinga ukuba ezi mfuno zimbini zokuqala zingasentla-kubandakanywa isakhono, ukufikelela, ukuqiqa, kunye nokucwangcisa-zisebenza njengoko kucetywayo, owona mngeni uphambili weokhestra yecandelo uxhomekeke kulawulo lolwazi. Inkqubo ye-agent kufuneka igcine ulwazi kumanqanaba ahlukeneyo: imisebenzi ephambili kunye neenjongo zayo, imeko yeenkqubo ezahlukeneyo ezifanelekileyo, imbali yokusebenzisana nomsebenzisi kunye nezinye iinkqubo zangaphandle, kunye nokunye okuninzi.

Nge-LLMs, sisebenzisa ingcamango "yefestile yomxholo" ukuchaza isethi yolwazi olukhoyo kumzekelo, ngokubanzi ngexesha elikhawulezayo. Oku kwahlukile kulwazi oluqulethwe kwi-prompt ngokwayo kwaye kwahlukile kulwazi lwangaphakathi lwe-LLM olwathi lwaqulunqwa ngexesha lenkqubo yoqeqesho lomfuziselo.

Kwimibhalo emide, iifestile zeemeko zinokucingelwa "njengembali yamva nje" yolwazi olukhoyo kwi-LLM ngexesha elikhawulezayo-oku kucacile kwi-architecture ye-LLM kunye nokukhuthaza. Ngaloo ndlela, uninzi lwe-LLM lunombono onecala elinye lomxholo, kwaye umxholo omdala uvele uphume efestileni ekuhambeni kwexesha.

Iiarhente zidinga inkqubo eyinkimbinkimbi yokulawula umxholo kunye nolwazi, ukwenzela ukuba kuqinisekiswe ukuba eyona nto ibalulekileyo okanye ingxamisekileyo umxholo wenziwa kuqala, nanini na xa i-arhente ifuna ukwenza isigqibo. Esikhundleni somxholo omnye we-monolithic, ii-arhente ze-AI kufuneka zilandele iindidi ezahlukeneyo zomxholo kumanqanaba ahlukeneyo okubaluleka.

Oku kunokufaniswa nememori kwiinkqubo zekhompyutheni, apho iintlobo ezahlukeneyo zokugcina-i-cache, i-RAM, kunye ne-hard drives-zikhonza iinjongo ezahlukeneyo ngokusekelwe ekufikelelekeni kunye nokuphindaphinda kokusetyenziswa. Kwiiarhente ze-AI, sinokwenza ngokwengqiqo umxholo ube ngamanqanaba amathathu aphambili:

1. Umxholo ophambili - Uluhlu lomsebenzi ongundoqo we-arhente okanye iinjongo. Oku kufuneka kuhlale kuphezulu kwengqondo, kukhokela zonke izenzo.

   
   

2. Umxholo othe ngqo -Imeko yeenkqubo eziqhagamsheleneyo, ezifanelekileyo kunye nokusingqongileyo okusondeleyo, kubandakanywa izixhobo ezifana neenkqubo zokuthumela imiyalezo, ukutya kwedatha, ii-APIs ezibalulekileyo, okanye i-imeyile yomsebenzisi kunye neekhalenda.

   
   

3. Umxholo wangaphandle - Ulwazi ngokubanzi, okanye naluphi na ulwazi olunokuba lufanelekile, kodwa olungacwangciswanga ngokucacileyo ukuba lube yinxalenye ephambili yenkqubo ye-arhente. Umxholo wangaphandle unokubonelelwa yinto elula njengokukhangela kwi-intanethi okanye kwiWikipedia. Okanye, kunokungxamiseka kwaye kube nzima, njengezinto ezingalindelekanga ezivela kwiindaba zomntu wesithathu okanye uhlaziyo, ezifuna ukuba iarhente ilungelelanise izenzo zayo ngamandla.

La manqanaba omxholo akaqinisekanga, imigca ephakathi kwayo ingaba mfiliba kakhulu, kwaye kukho ezinye iindlela eziluncedo zokuchaza iintlobo zomxholo-kodwa le ngcamango yesakhiwo iluncedo kwingxoxo yethu apha.

IziSeko zoLondolozo zoLawulo lweemeko

Iimfuno zokugcina zee-arhente ze-AI ziyahluka ngokuxhomekeke kuhlobo lomxholo olawulwayo. Inqanaba ngalinye-eliphambili, elithe ngqo kunye nangaphandle-lifuna izakhiwo zedatha ezahlukeneyo, iindlela zokubuyisela, kunye nokuhlaziywa rhoqo. Umceli mngeni ophambili kukuqinisekisa ukufikelela okusebenzayo, ukuzingisa kwexesha elide, kunye nohlaziyo oluguquguqukayo ngaphandle kokulayisha kakhulu umbhobho wokusetyenzwa kwearhente.

Kunokuba uphathe umxholo njengequmrhu le-monolithic, ii-arhente ze-AI zixhamla kwi-architecture yogcino oluxutyiweyo oludibanisa iimodeli zedatha ezicwangcisiweyo nezingalungiswanga. Oku kuvumela ukukhangelwa okukhawulezayo, ukufunyanwa kwe-semantic, kunye nokuzingisa okukhawulezileyo, ukuqinisekisa ukuba umxholo ofanelekileyo uyafumaneka xa kufuneka ngelixa kuncitshiswa ukusetyenzwa kwedatha okungafunekiyo.

Umxholo oPhambili: Uluhlu lweMisebenzi kunye neenjongo ze-Agent

Umxholo ophambili uquka iinjongo eziphambili ze-arhente kunye nemisebenzi esebenzayo-isiseko esiqhuba ukuthathwa kwezigqibo. Olu lwazi kufuneka luzingise, lucwangciseke kakhulu, kwaye lubuze lula, njengoko lukhokela zonke iintshukumo zearhente.

Iimfuno ezinokubakho zokugcina:

• Oovimba beenkcukacha zentengiselwano (ixabiso elingundoqo okanye iivenkile zamaxwebhu) kuluhlu lwemisebenzi ecwangcisiweyo kunye noluhlu lweenjongo.
• Isalathiso se-latency esezantsi ukuxhasa ukujonga ngokukhawuleza kwemisebenzi esebenzayo.
• Uhlaziyo oluqhutywa sisiganeko ukuqinisekisa ukuba imisebenzi ibonisa inkqubela yexesha lokwenyani.

Ukuphunyezwa kwe-arhente yomzekelo

Umncedisi ocwangcisayo olawula umgca womsebenzi kufuneka agcine:

• Imisebenzi ethe gqolo (umzekelo, “Cwangcisa intlanganiso no-Alex”) ngohlaziyo lobume.
• Imbali yokwenziwa (umzekelo, “I-imeyile yokuqala ithunyelwe, ilindele impendulo”).
• Izinto eziphambili kunye nokuxhomekeka, ukuqinisekisa ukuba imisebenzi engxamisekileyo ibonakaliswa kuqala.

Ivenkile yedatha esasazwayo, efumaneka kakhulu iqinisekisa ukuba imisebenzi ilandelwa ngokuthembekileyo, njengokuba i-arhente iqhuba iziganeko ezitsha kunye nokuhlaziywa kweemeko.

Umxholo othe ngqo: Imo yeeNkqubo eziQhagamshelweyo

Umxholo othe ngqo uquka imeko yangoku yeenkqubo ezifanelekileyo-iikhalenda, iiplatifomu zemiyalezo, ii-API, ii-database, kunye neminye imithombo yedatha yexesha langempela. Ngokungafaniyo nomxholo oyintloko, umxholo othe ngqo uyaguquguquka kwaye kaninzi ufuna indibaniselwano yezisombululo ezicwangcisiweyo kunye nexesha langempela lokugcina.

Iimfuno ezinokubakho zokugcina:

• Uluhlu lweenkcukacha zoluhlu lweziganeko zeziganeko kunye nexesha lokwenene lokulandelela isimo.
• I-Caching layers yeemeko zenkqubo ezifikelelwa rhoqo.
• Ukubuyiswa okusekwe kwiVector kwimibuzo yomxholo kunxibelelwano lwamva nje.

Uzalisekiso lwe-arhente yomzekelo:

I-arhente yenkxaso ye-AI elandelela unxibelelwano lomsebenzisi kufuneka igcine:

• Imbali yencoko yexesha lokwenyani kwivenkile ekwimemori.
• Imeko yeseshoni (umzekelo, iinkcukacha zetikiti eziqhubekayo) kwisiseko sedatha yexesha.
• I-API yokuphendula i-cache yokujonga inkqubo yangaphandle, ukuphepha imibuzo engafunekiyo.

Ngokuqulunqa ukugcinwa komxholo othe ngqo kunye nendibaniselwano yokugcinwa kwedatha yexesha elide kunye nexesha elide, ii-arhente ze-AI zingenza ngokuqaphela okusingqongileyo ngaphandle kokubambezeleka okugqithisileyo.

Umxholo waNgaphandle: ukuFumana ulwazi kunye noLungelelwaniso

Umxholo wangaphandle uquka ulwazi oluphangaleleyo kunye nohlaziyo olungalindelekanga oluvela kwimithombo engaphandle kolawulo lwangoko lomenzeli. Oku kunokususela kwimibuzo yokukhangela kwimfuno ukuya kwidatha yangaphandle eguquguqukayo, efuna indlela eguquguqukayo yokugcina nokubuyisela. Ngokungafani neemeko eziphambili kunye nezithe ngqo, ezidityaniswe ngokusondeleyo kwimisebenzi eqhubekayo ye-arhente kunye neenkqubo ezidibeneyo, umxholo wangaphandle uhlala ungacwangciswanga, ukhulu, kwaye uguquguquka kakhulu.

Imiba yogcino olunokwenzeka:

• Iivenkile zamaxwebhu kunye neziseko zolwazi zezinto ezizingisayo, ezicwangcisiweyo zereferensi.
• Ukukhangela kweVector yokubuza iiseti zedatha enkulu yamaxwebhu, ngaphakathi okanye ngaphandle.
• Ukubuyiswa-kwandiswa kwesizukulwana (RAG ) ukufumana ulwazi olufanelekileyo ngaphambi kokuphendula.
• Ukusasaza kunye nokungeniswa okuqhutywa ngumcimbi wokuhlaziywa kwexesha langempela ukusuka kwimithombo yedatha yangaphandle.

Uzalisekiso lwe-arhente yomzekelo:

Umncedisi wobuqu ohlanganisa ingxelo malunga nenzululwazi yamva nje efunyenwe kuphando lokutshintsha kwemozulu kufuneka:

• Fumana amanqaku enzululwazi kwimithombo yangaphandle, ukuhluza ukufaneleka ngokusekelwe kumagama angundoqo okanye ukufana kwe-vector.
• Hlalutya ubudlelwane phakathi kwamaphepha , uchonga iintsingiselo usebenzisa igrafu yolwazi.
• Shwankathela iimbono eziphambili usebenzisa i-LLM-based retrieval-augmented generation.
• Landela uhlaziyo lwamva nje ngokubhalisela upapasho lwexesha lokwenyani kunye nemithombo yeendaba.

Ngokucwangcisa ukugcinwa kweemeko zangaphandle malunga nokubuyiswa ngokukhawuleza kunye nentlangano ye-semantic, ii-arhente ze-AI zinokuziqhelanisa ngokuqhubekayo nolwazi olutsha ngelixa ziqinisekisa ukuba idatha efunyenweyo ihlala ifanelekile, ithembekile, kwaye iyasebenza.

Ukugcinwa kweHybrid kuMxholo-Aware AI Agents

Ukuyila ii-arhente ze-AI eziqondayo zifuna ulungelelwaniso olucokisekileyo phakathi kokufikelela ngokufanelekileyo kulwazi olubalulekileyo kunye nokuphepha imemori okanye ukulayisha ngaphezulu. Iiarhente ze-AI kufuneka zithathe isigqibo sokuba zigcinwe nini, zibuyiselwe nini, kwaye ziqhube umxholo ngokuguquguqukayo ukuze kunyuswe ukwenziwa kwezigqibo.

Uyilo lokugcinwa kwe-hybrid-ukudibanisa ukuthengiselana, i-vector, uchungechunge lwexesha, kunye neemodeli eziqhutywe kwisiganeko-ivumela ii-arhente ze-AI ukuba zigcine ukuphikelela komxholo, ukubuyisela ukusebenza kakuhle, kunye nobukrelekrele obuguquguqukayo, konke oku kubalulekile kukuzimela kwinqanaba. Ukufezekisa le bhalansi kufuna izicwangciso ezicwangcisiweyo kuzo zonke iinkalo ezintathu eziphambili:

1. Ukubambezeleka ngokuchasene nokuzingisa - Iimeko ezifikelelwa rhoqo (umzekelo, iindawo zemisebenzi esebenzayo) kufuneka zihlale kwindawo yokugcina ixesha elide, ngelixa ulwazi olungafuneki kangako kodwa oluyimfuneko (umzekelo, ukusebenzisana kwembali) kufuneka lufunyanwe ngokufunwa kugcino lwexesha elide.

   
   

2. Ulwakhiwo ngokuchasene nedatha engacwangciswanga - Imisebenzi, iinjongo, kunye nenkqubo yelizwe ixhamla kugcino olucwangcisiweyo (umzekelo, ixabiso eliphambili okanye ugcino lweenkcukacha zamaxwebhu), ngelixa ukufunyaniswa kolwazi olubanzi kufuna ukulungiswa okungacwangciswanga kunye nobudlelwane begrafu ukuze ubambe umxholo ngokufanelekileyo.

   
   

3. Ixesha lokwenyani xa lithelekiswa nokwazisa ngokwembali - Eminye imixholo ifuna ukubekwa esweni okuqhubekayo (umzekelo, iimpendulo eziphilileyo ze-API), kanti ezinye (umzekelo, izigqibo zangaphambili okanye iingxelo) kufuneka zithathwe kuphela xa zifanelekile kumsebenzi wangoku we-arhente.

Ukunikezelwa kwezi ntlobo ezahlukeneyo zeemeko, ii-arhente ze-AI zifuna indlela ecwangcisiweyo yokugcina nokufikelela kulwazi. Ukuthembela kuphela kwiifestile ze-LLM akusebenzi, njengoko kunciphisa amandla e-arhente ukulandelela ukusebenzisana kwexesha elide kunye neemeko eziguqukayo. Endaweni yoko, umxholo kufuneka ngokuzingileyo ugcinwe, ufunyanwe ngamandla, kwaye ubekwe phambili ngokusekwe kukufaneleka kunye nokungxamiseka.

• Umxholo ophambili (imisebenzi kunye neenjongo) -Igcinwe koovimba bentengiselwano ukuze ilandelwe ngendlela ecwangcisiweyo kwaye ibhekiselele kuwo wonke umjikelo wengqiqo.

• Umxholo othe ngqo (ubume benkqubo kunye nedatha esebenzayo) - Igcinwe ngexesha langempela ngokusebenzisa i-caching, ukugcinwa kwexesha, okanye ukuhlaziywa kwesiganeko.

• Umxholo wangaphandle (ulwazi kunye nohlaziyo oluguquguqukayo) - Ukubuzwa kwimfuno ngophendlo lwe-vector, i-retrieval-augmented generation (RAG), okanye ulwazi olusekwe kwigrafu.

Ngokwesiqhelo, iimodeli zememori ezinamanqanaba amaninzi adibanisa i-cache yexesha elifutshane, i-database eqhubekayo, kunye neendlela zokubuyisela zangaphandle ziyafuneka kwi-architecture ye-arhente ye-AI. Ngokusebenzisa indlela yokugcina edibeneyo, iiarhente ze-AI zinoku:

• Gcina ulwazi lwexesha lokwenyani lweenkqubo ezisebenzayo.
• Fumana ulwazi lwembali kuphela xa kufanelekile.
• Lungisa izinto eziphambili ngokubaluleka ngokusekelwe kwiimfuno eziguqukayo.

Ngokudibanisa ezi zicwangciso zokugcina, ii-arhente ze-AI zinokusebenza ngokuzimeleyo, zigcine ulwazi lomxholo kwixesha elide, kwaye ziphendule ngokuguquguqukayo kulwazi olutsha-ukubeka isiseko seenkqubo ezikrelekrele ngokwenene kunye ne-scalable arhente.

Hybrid Storage Solutions

Ukuphumeza i-architecture yokugcinwa kwe-hybridi yee-arhente ze-AI kufuna ukukhetha i-database efanelekileyo kunye nezixhobo zokugcina ukusingatha iintlobo ezahlukeneyo zeemeko ngokufanelekileyo. Olona khetho lungcono luxhomekeke kwizinto ezifana neemfuno zokulinda, ukukala, ukuhambelana kwesakhiwo sedatha, kunye neendlela zokubuyisela.

Inkqubo yokugcina i-arhente ye-AI eyilwe kakuhle ibandakanya:

• Uluhlu lweenkcukacha zolwazi olucwangcisiweyo, oluzingisileyo lokulandelela umsebenzi.
• Uluhlu lwexesha kunye nokugcinwa kwesiganeko esiqhutywa kwisiganeko sokujonga inkqubo yexesha langempela.
• Uphendlo lweVector kunye nokufunyanwa kolwazi lokufikelela kwidatha eguquguqukayo, engacwangciswanga.
• I-Caching kunye ne-in-memory database yokufikelela ngokukhawuleza kwimemori yexesha elifutshane.

Makhe sihlolisise inkalo nganye kwezi.

Iidatabase zeNtengiselwano kunye noSasazo

Ii-arhente ze-AI zifuna i-scalable, i-database yentengiselwano ekhoyo kakhulu yokugcina imisebenzi, iinjongo, kunye nemethadatha eyakhiwe ngokuthembekileyo. Ezi nkcukacha zedatha ziqinisekisa ukuba umxholo ophambili uhlala ufumaneka kwaye unemibuzo ngokufanelekileyo.

• I- Apache Cassandra® -I-database ye-NoSQL esasaziweyo eyenzelwe ukufumaneka okuphezulu kunye nokunyamezela iimpazamo. Ilungele ukulawula uluhlu lwemisebenzi ecwangcisiweyo kunye nokulandelwa kwenjongo ye-arhente kwisikali.

• I-DataStax Astra DB - I-database elawulwayo-njengenkonzo (i-DBaaS) eyakhelwe kwi-Cassandra, ibonelela nge-elastic scalability kunye ne-multi-replication replication kwizicelo ze-AI ezifuna ukuqina okuphezulu.

• I-PostgreSQL -Isiseko sedatha esithandwayo esineziqinisekiso eziqinileyo, ezifanelekileyo kwi-metadata ye-arhente ehleliweyo, iilogi zemisebenzi eqhubekayo, kunye nokunyanzeliswa komgaqo-nkqubo.

Uthotho lwexesha kunye noGcino oluqhutywa nguMnyhadala

Ukubeka iliso kwinkqubo yexesha lokwenyani, iiarhente ze-AI zifuna ugcino-lwazi olulungiselelwe ukugawulwa kwemithi, ukulandelwa kwesiganeko, kunye nokuzingisa korhulumente.

• I-InfluxDB - I-database ekhokelayo yexesha elide eyenzelwe ukungeniswa kwesantya esiphezulu kunye nemibuzo esebenzayo, okwenza kube yinto efanelekileyo yokungena kwi-arhente ye-AI kunye nohlaziyo lwenkqubo yangaphandle.

• I-TimescaleDB - Ulwandiso lwe-PostgreSQL olulungiselelwe ubuninzi bexesha lomsebenzi, olulungele ukulandelela utshintsho kwi-arhente ye-AI yokuhamba komsebenzi kunye neziganeko zenkqubo.

• I-Apache Kafka + kSQLDB -Iqonga ledatha lokusasaza elivumela ii-arhente ze-AI ukuba zidle, ziqhube, kwaye zisabele kwiziganeko zexesha langempela ngokufanelekileyo.

• I-Redis Streams -Isisombululo esikhaphukhaphu sokusingatha umcimbi wexesha lokwenyani kunye nomgca womyalezo, oluncedo ekugcineni iiarhente ze-AI zinolwazi ngohlaziyo olutsha njengoko lusenzeka.

Uphendlo lweVector yokuFumana ulwazi

Iiarhente ze-AI ezisebenza ngolwazi olungacwangciswanga zifuna iindlela ezisebenzayo zokugcina, ukukhangela, kunye nokubuyisela okuzinzisiweyo kwimisebenzi efana nokukhangela kwe-semantic, ukuthelekisa ukufana, kunye ne-retrieval-augmented generation (RAG). Inkqubo yokukhangela i-vector eyenziwe kakuhle yenza ukuba ii-arhente zikhumbule unxibelelwano olufanelekileyo lwangaphambili, amaxwebhu, okanye iinyani ngaphandle kokulayisha kakhulu kwimemori okanye iifestile zeemeko.

• I-DataStax Astra DB - I-database ye-scalable, elawulwayo ye-vector eyakhelwe kwi-Cassandra, inikezela ngokufana okuphezulu kokufuna ukufana kunye nokubuyiswa kwe-multimodal. I-Astra idibanisa ukomelela okusasazwayo kunye nesakhono sokukhangela i-vector, iyenza ibe lolona khetho luphezulu kwiiarhente ze-AI ezifuna ukucubungula ukufakwa ngokufanelekileyo ngelixa iqinisekisa ubungakanani behlabathi kunye nokufumaneka okuphezulu.

• I-Weaviate - Isiseko sedatha ye-vector yefu eyenzelwe ukukhangela kwe-semantic kunye nokufunyanwa kwedatha ye-multimodal. Ixhasa iindlela zokukhangela ezixubileyo kwaye idibanisa kakuhle kunye neegrafu zolwazi, okwenza kube luncedo kwii-arhente ze-AI ezixhomekeke kwingqiqo yomxholo.

• I-FAISS (Ukukhangela okufanayo kwe-Facebook AI) - Ithala leencwadi elivulekileyo lokukhangela ummelwane osondeleyo, ehlala ifakwe kwimibhobho ye-AI yokukhangela i-vector ekhawulezayo kwiiseti ezinkulu zedatha. Ngelixa ingekho i-database epheleleyo, i-FAISS ibonelela ngesisombululo esilula, esinesantya esiphezulu sokukhangela okufanayo kwendawo.

I-Caching kunye ne-In-Memory Storage

Iiarhente ze-AI zifuna ukufikelela okuphantsi kwe-latency kumxholo osetyenziswa rhoqo, okwenza i-caching ibe yinxalenye ebalulekileyo ye-hybrid storage architectures.

• I-Redis - I-high-performance in-memory key-value store, esetyenziswa ngokubanzi kwi-caching yemeko yexesha elifutshane kunye nokulawulwa kweeseshoni kwii-arhente ze-AI.

• I-Memcached - Inkqubo ye-caching elula kodwa esebenzayo ehambisa ngokukhawuleza ebonelela ngokukhawuleza kwidatha ye-arhente ye-AI esetyenziswa rhoqo.

Ngokudibanisa ezi zisombululo zahlukeneyo zokugcina, iiarhente ze-AI zinokulawula ngokufanelekileyo inkumbulo yexesha elifutshane, ulwazi oluqhubekayo, kunye nohlaziyo lwexesha lokwenyani, ukuqinisekisa ukuthathwa kwezigqibo ezingenamthungo kwinqanaba. Ukudityaniswa kwedatha yentengiselwano, ukugcinwa kwexesha, ukukhangela i-vector, kunye ne-caching ivumela ii-agent ukuba zilungelelanise isantya, i-scalability, kunye nokwaziswa komxholo, ukulungelelanisa ngokuguquguqukayo kwigalelo elitsha.

Njengoko izicelo eziqhutywa yi-AI ziqhubeka nokuvela, ukukhetha i-architecture efanelekileyo yokugcina i-hybrid iya kuba yinto ebalulekileyo ekwenzeni ukuzimela, ukusabela, kunye neenkqubo ezihlakaniphile ze-arhente ezinokusebenza ngokuthembekileyo kwiimeko ezinzima kunye nezihlala zitshintsha.

Ikamva leearhente ze-AI ezineHybrid Database

Njengoko iinkqubo ze-AI zikhula nzima ngakumbi, i-database ye-hybridi iya kuba yinto ebalulekileyo yokulawula imemori yexesha elifutshane kunye nexesha elide, idatha ehleliweyo kunye neyokungacwangciswanga, kunye nexesha lokwenyani kunye nokuqonda kwembali. Ukuqhubela phambili kwi-retrieval-augmented generation (RAG), i-semantic indexing, kunye ne-inference yokusabalalisa yenza i-AI agents zisebenze ngakumbi, zihlakaniphile, kwaye ziguquguquke. Iiarhente ze-AI zexesha elizayo ziya kuthembela kugcino olukhawulezayo, olunokwehla, kunye nokuqonda umxholo ukuze kugcinwe ukuqhubeka nokwenza izigqibo ezinolwazi ngokuhamba kwexesha.

Kutheni le Hybrid Databases?

Iiarhente ze-AI zidinga izisombululo zokugcina ezilawula ngokufanelekileyo iintlobo ezahlukeneyo zomxholo ngelixa uqinisekisa isantya, ukulinganisa, kunye nokuqina. I-database ye-Hybrid inikezela ngeyona nto ingcono yehlabathi-idatha ekhawulezayo ehleliweyo kunye nokubuyiswa kweemeko ezinzulu-ezenza zibe sisiseko kwiinkqubo ezikrelekrele ze-AI. Baxhasa ukukhangela okusekwe kwi-vector kugcino lolwazi lwexesha elide, ukujongwa okuphantsi kwe-latency transactional, uhlaziyo lwexesha lokwenyani oluqhutywa yisiganeko, kunye nokusabalalisa i-scalability yokunyamezela impazamo.

Ukwakha i-Scalable AI Data Infrastructure

Ukuxhasa iiarhente ezikrelekrele ze-AI, abaphuhlisi kufuneka bayile izakhiwo zokugcina ezidibanisa iimodeli ezininzi zedatha zolawulo lweemeko ezingenamthungo:

• Ukukhangela kweVector kunye nedatha yekholomu - gcina umxholo we-semantic ecaleni kwemethadatha ecwangcisiweyo yokufumana ngokukhawuleza

• Ukuhamba komsebenzi okuqhutywa ngumsitho - ukusasaza uhlaziyo lwexesha langempela ukugcina iiarhente ze-AI zisazi ngokutshintsha kwedatha

• Ubungakanani behlabathi kunye nokomelela - ukusasaza kwiinethiwekhi ezisasaziweyo zokufumaneka okuphezulu kunye nokunyamezela iimpazamo

  
  

Ngokudibanisa ukusetyenzwa kwentengiselwano, ukukhangela i-vector, kunye nohlaziyo lwexesha lokwenyani, hybrid databases ezifana DataStax Astra DB bonelela ngesiseko esisiso senkumbulo ye-arhente ye-AI, ukuqonda umxholo, kunye nokwenziwa kwezigqibo. Njengoko izicelo eziqhutywa yi-AI ziguquka, izisombululo zokugcinwa kwe-hybrid ziya kubaluleka ekuvumela ukuba ukuzimela, ii-arhente ze-AI ezityebileyo ezisebenza ngokuthembekileyo kwiindawo eziguquguqukayo, ezinomthamo wedatha.

Ibhalwe nguBrian Godsey, DataStax