I-Artificial Intelligence (AI) iyayitshintsha indlela esenza ngayo kwaye siyivavanya idatha. Kwaye, i-database ye-vector yenye yezixhobo eziphambili eziqhuba olu tshintsho.
Ezi nkcukacha zogcino-lwazi zisebenza ngokugqwesileyo ekugcineni nasekufumaneni umelo lwedatha olunomgangatho ophezulu.
Banamandla okudlala indima ebalulekileyo kwimpumelelo yezicelo ze-AI ezifana nokulungiswa kolwimi lwendalo, ukuqaphela umfanekiso, kunye neenkqubo zokucebisa.
Kule post, siza kujonga intsimi enomdla yogcino lwedatha kwi-AI kwaye kutheni ibaluleke kakhulu kwizazinzulu zedatha kunye neengcali zokufunda ngomatshini.
Kutheni oovimba beenkcukacha zobudlelwane benganelanga kwizicelo ze-AI
Ngokuqhelekileyo sigcina kwaye sikhuphe idatha sisebenzisa ugcino lwedatha oluqhelekileyo. Nangona kunjalo, ezi nkcukacha zolwazi azisoloko zilungele ukubonakaliswa kwedatha ephezulu, eyimfuno eqhelekileyo kwizicelo ezininzi ze-AI.
Ukusetyenzwa kwezixa ezikhulu zedatha engacwangciswanga esoloko isetyenziswa kwi-AI kunokuba ngumngeni ngenxa yolu luhlu lwendalo olucwangcisiweyo.
Iingcali zazifuna ukunqanda ukusetshwa okulibazisekayo nokungasebenzisi. Ke, ukoyisa le miceli mngeni, basebenzise izisombululo ezifana nokucaba izakhiwo zedatha. Nangona kunjalo, le yayiyinkqubo echitha ixesha kwaye ineempazamo.
Indlela esebenzayo ngakumbi yokugcina kunye nokubuyisela idatha ye-high-dimensional data iye yavela ngokunyuka kwe-database ye-vector. Ngale ndlela, kunokwenzeka ukuba nezicelo ze-AI ezilungelelanisiweyo nezinempumelelo.
Ngoku, makhe sibone ukuba zisebenza njani ezi nkcukacha zedatha.
Yintoni kanye kanye oovimba beenkcukacha zevekhtha?
Oovimba bogcino-lwazi beVector ngoovimba beenkcukacha ezikhethekileyo ezenzelwe ukugcina nokuphatha izixa ezikhulu zedatha enomgangatho ophezulu ngokohlobo lweevekhtha.
IiVectors luphawu lwedatha yemathematika echaza izinto ezisekelwe kwiimpawu zazo ezahlukeneyo okanye iimpawu.
Ivektha nganye imele indawo enye yedatha, njengegama okanye umfanekiso, kwaye yenziwe yingqokelela yamaxabiso echaza iimpawu zayo ezininzi. Ezi ziguquko ngamanye amaxesha zaziwa ngokuba "ziimpawu" okanye "imilinganiselo."
Umfanekiso, umzekelo, unokumelwa njengevektha yamaxabiso eepixels, kodwa isivakalisi sisonke sinokuboniswa njengevektha yofakelo lwamagama.
Oovimba bogcino-lwazi beVektha basebenzisa iindlela zokwalathisa ukuthomalalisa ukufunyanwa kweevektha ezifana nevektha yombuzo othile. Oku kuluncedo ngakumbi kwi yokufunda umatshini izicelo, njengoko uphendlo olufanayo lusetyenziswa rhoqo ukufumana amanqaku anokuthelekiseka edatha okanye ukuvelisa iingcebiso.
Imisebenzi yangaphakathi yeeDatha zeVector
Ugcino lwedatha yeVector lusetyenziselwa ukugcina kunye nesalathiso se-high-dimensional vectors eveliswa ngobuchule obunje ukufunda okunzulu. Ezi vektha ziyimifanekiso yamanani yezinto ezintsonkothileyo zedatha eziguqulelwa kwindawo enomgangatho ophantsi ngelixa igcina ulwazi olubalulekileyo ngobuchule bokuzinzisa.
Ke, oovimba beenkcukacha zevektha bakhelwe ukulungiselela ulwakhiwo oluthile lokuzinzisa i-vector, kwaye basebenzisa i-indexing algorithms ukukhangela ngokufanelekileyo kunye nokubuyisela ii-vectors ngokusekwe ngokufana kwazo kwi-vector yombuzo.
Ingaba isebenza kanjani?
Oovimba beenkcukacha beVektha basebenza ngokufanayo kwiibhokisi zomlingo ezigcina kwaye zicwangcisa izinto ezintsonkothileyo zedatha.
Basebenzisa iindlela ze-PQ kunye ne-HNSW ukuchonga nokufumana ulwazi oluchanekileyo ngokukhawuleza. I-PQ isebenza ngokufanayo kwisitena se-Lego, i-condensing vectors ibe ngamacandelo amancinci ukunceda ekukhangeleni okufanayo.
I-HNSW, kwelinye icala, iphuhlisa iwebhu yekhonkco ukuququzelela ii-vectors kwi-hierarchy, ukwenza ukuhamba kunye nokukhangela kube lula. Ezinye iinketho zokuyila, ezinjengokongeza kunye nokukhupha ii-vectors ukubona ukufana kunye nomahluko, zikwaxhaswa yi-database ye-vector.
Zisetyenziswa njani iiDatha zeVector kwi-AI?
Oovimba beenkcukacha zeVektha banesakhono esikhulu kwindawo ye kukubhadla okungeyonyani. Zisinceda ukuba silawule ngokufanelekileyo amanani amakhulu edatha kwaye sixhase imisebenzi entsonkothileyo efana nokukhangela okufanayo kunye ne-vector arithmetic.
Ziye zaba zizixhobo eziyimfuneko kuluhlu olubanzi lwezicelo. Oku kubandakanya ukusetyenzwa kolwimi lwendalo, ukuqondwa kwemifanekiso, kunye neenkqubo zokucebisa. Ufakelo lweVektha, umzekelo, lusetyenziswa kulwimi lwendalo ukuze kuqondwe intsingiselo kunye nomxholo wesicatshulwa, ukuvumela iziphumo zophando ezichanekileyo nezifanelekileyo.
Ugcino lwedatha yeVector ekuqatshelweni kwemifanekiso inokukhangela imifanekiso ethelekisekayo ngokufanelekileyo, nakwiiseti zedatha ezinkulu. Basenokunikezela ngezinto ezinokuthelekiswa okanye ulwazi kubathengi ngokusekelwe kwizinto abazithandayo kunye nokuziphatha kwiinkqubo zokucebisa.
IiNgcebiso eziGqwesileyo zokuSebenzisa iiDatha zeVector kubukrelekrele bokwenziwa
Ukuqala, i-input vectors kufuneka iqhutywe ngaphambili kwaye ibe yesiqhelo ngaphambi kokuba igcinwe kwiziko ledatha. Oku kunokonyusa ukuchaneka kwevektha kunye nokusebenza.
Okwesibini, i-algorithm ye-indexing efanelekileyo kufuneka ikhethwe ngokuxhomekeke kwimeko yokusetyenziswa komntu kunye nokusabalalisa idatha. ii-algorithms ezahlukeneyo zineendlela ezahlukeneyo zokurhweba phakathi kokuchaneka kunye nesantya, kwaye ukukhetha efanelekileyo kunokuba nefuthe elikhulu ekusebenzeni kokukhangela.
Okwesithathu, ukuqinisekisa ukusebenza kakuhle, isiseko sedatha ye-vector kufuneka sibekwe iliso kwaye sigcinwe rhoqo. Oku kubandakanya ukuphinda kufakwe isalathisi kwisiseko sedatha njengoko kuyimfuneko, ukulungisa kakuhle izalathisi iiparamitha, kunye nokubekw'esweni ukusebenza kokukhangela ukufumanisa nokusombulula nabuphi na ubunzima.
Ekugqibeleni, ukwandisa amandla ezicelo ze-AI, kucetyiswa ukuba kuqeshwe i-database ye-vector exhasa iimpawu eziyinkimbinkimbi ezifana ne-vector arithmetic kunye nokukhangela okufanayo.
Kutheni kufuneka usebenzise iDatabase yeVector?
Eyona njongo iqhelekileyo yokusebenzisa i-database ye-vector kukukhangela i-vector kwimveliso. Ukufana kwezinto ezininzi kumbuzo wokukhangela okanye umxholo uthelekiswa kolu hlobo lokukhangela. I-database ye-vector inamandla okuthelekisa ukufana kwezi zinto ukufumana eyona ngqamaniso isondeleyo ngokuguqula umxholo wesihloko okanye umbuzo ube yi-vector usebenzisa imodeli yokufakela i-ML efanayo.
Oku kuvelisa iziphumo ezichanekileyo ngelixa uphepha iziphumo ezingabalulekanga eziveliswa bubuchwepheshe obuqhelekileyo bokukhangela.
Umfanekiso, iAudio, uPhando oluFanelekileyo lweVidiyo
Imifanekiso, umculo, ividiyo, kunye nolunye ulwazi olungacwangciswanga kunokuba nzima ukuhlulahlula kunye nokugcina kwisiseko sedatha esiqhelekileyo. Ugcino lwedatha yeVector luyimpendulo egqwesileyo yoku kuba banokukhangela izinto ezinokuthelekiswa ngokukhawuleza nakwiiseti zedatha ezinkulu. Le ndlela ayifuni mntu ukuthegiswa kwedatha okanye ukuleyibhelishwa kwaye unokukhangela ngokukhawuleza eyona midlalo ikufutshane ngokusekwe kumanqaku afanayo.
Iinjini zokuQala kunye neNcome
Oovimba bogcino-lwazi beVektha bakwakulungele kakuhle ukusetyenziswa kwiinkqubo zodidi kunye neengcebiso. Zingasetyenziselwa ukucebisa izinto ezinokuthelekiswa nokuthengwa kwangaphambili okanye into yangoku umthengi ayijongileyo.
Endaweni yokuxhomekeka kuhluzo oludityanisiweyo okanye uludwe oludumileyo, ukusasaza iinkonzo zemidiya zinokunyusa iireyithingi zengoma yomsebenzisi ukubonelela ngeengcebiso ezihambelana ngokugqibeleleyo ezenzelwe umntu ngamnye. Bangakwazi ukufumana iimveliso ezinokuthelekiswa ngokusekwe kwimidlalo ekufutshane.
Ukukhangela kwe-Semantic
Uphendlo lweSemantic sisixhobo esinamandla sokubhaliweyo kunye noxwebhu oluhamba ngaphaya kophando lwegama elingundoqo eliqhelekileyo. Intsingiselo kunye nomxholo wemitya yokubhaliweyo, amabinzana, kunye namaxwebhu apheleleyo anokuqondwa ngokusebenzisa i-database ye-vector ukugcina kunye ne-index ye-vector embedings evela kwi-Natural. Imifuziselo yokwenziwa koLwimi.
Ke, abasebenzisi baya kuba nakho ukufumana into abayifunayo ngokukhawuleza ngaphandle kokuqonda ukuba idatha ihlelwa njani.
Ubuchwephesha beVector Databases
Kukho itekhnoloji yedatha yedatha eyahlukeneyo ekhoyo, nganye ineseti yayo yeenzuzo kunye nokungalunganga.
IPinecone, Faiss, Iyacaphukisa, Milvus, yaye Hnswlib zezinye zezinto ezinokwenzeka ezidumileyo.
IPinecone
Yisiseko sedatha yevector esekwe kwilifu. Ungaphuhlisa usetyenziso olufana nexesha lokukhangela. Ivumela abasebenzisi ukuba bagcine kwaye baphonononge ukushumeka kwevektha enomgangatho ophezulu nge-millisecond latencies.
Oku kuyenza ilungele izicelo ezifana neenkqubo zokucebisa, ukukhangela umfanekiso kunye nevidiyo, kunye nokusetyenzwa kolwimi lwendalo.
Iimpawu eziphambili zePinecone ziquka i-indexing ngokuzenzekelayo, uhlaziyo lwexesha langempela, i-query auto-tuning, kunye ne-REST API yokusebenzisana okulula kunye neenkqubo zangoku. Uyilo lwayo lwenzelwe ukulinganisa kunye nokomelela. Ungalawula ngokulula izixa ezikhulu zedatha ngelixa ugcina ukufumaneka okuphezulu.
Faiss
Yiphakheji yomthombo ovulekileyo we-Facebook obonelela ngokuphunyezwa kwe-cutting-edge ye-indexing kunye nokukhangela i-algorithms kwiivectors ezinkulu.
Ixhasa iindlela ezininzi zokukhangela i-vector. Enye yeenzuzo zayo eziphambili sisantya kunye nokulinganisa, okuvumela ukukhangela ngokukhawuleza nakwiiseti zedatha ezineebhiliyoni zeevektha.
Iyacaphukisa
I-Annoy, kwelinye icala, yilayibrari ye-C ++ eyakhelwe ukukhangela ummelwane osondeleyo osondeleyo. Kulula ukuyisebenzisa kwaye iphumeze ubuchule bomthi wentelekelelo engaqhelekanga ngokukhawuleza.
I-Annoy yilayibrari encinci yenkumbulo efanelekile ukuba isetyenziswe kwiimeko ezinobuncwane.
Milvus
I-Milvus yindawo yogcino lwedatha esimahla kunye nevulekileyo yokugcina nokukhangela iivektha ezinkulu. Ixhasa iindlela ezahlukeneyo zokukhomba, kubandakanya i-IVF kunye ne-HNSW, kwaye inokulawula ngokulula izigidi zeevekhtha.
Ukukwazi kwayo ukukhawulezisa i-GPU, enokuthi ikhawulezise kakhulu inkqubo yokukhangela, yenye yezona zinto zibalaseleyo.
Kulula ukuba lolona khetho lungcono xa uthatha isigqibo sokukhetha imveliso yogcino lwedatha ye-vector.
Hnswlib
I-Hnswlib lelinye ithala leencwadi elinomthombo ovulekileyo obonelela ngothungelwano lwehlabathi oluncinci olunolwandle olunokuhanjiswa ngokukhawuleza ukuze kufakwe izalathiso kunye nokukhangela iivektha ezinomgangatho ophezulu.
Ilungile kwiimeko apho indawo yevektha ihlala iguquka, kwaye ibonelela ngesalathiso esongezelekayo ukugcina isalathiso sifikelele ngoku kunye neevektha ezintsha. Ikwanokuhlengahlengiswa ngokugqithisileyo, ivumela abasebenzisi ukuba bahlengahlengise ibhalansi yokuchaneka kunye nesantya.
Utsalo olunokwenzeka
Ngelixa i-database ye-vector ineenzuzo ezininzi, zikwanazo nezingeloncedo ezibalulekileyo. Enye inkxalabo enokubakho sisixa esiphezulu sogcino olufunekayo ukuze kulawulwe ukufakwa kwe-vector.
Ngaphaya koko, oovimba beenkcukacha beVector basenokusokola kwiintlobo ezithile zedatha, njengemibuzo emifutshane okanye ekhethekileyo kakhulu. Okokugqibela, ukuseta kunye nokuphucula ezi nkcukacha zolwazi kunokubandakanya isakhono esikhulu, kubenza bafikeleleke kancinci kwabanye abasebenzisi.
Yintoni iNqanaba elilandelayo?
Kukho izinto ezongezelelekileyo ezinokwenzeka kumphezulu njengoko uvimba weenkcukacha weVector uqhubeka nokuvela. Omnye ummandla apho inkqubela ebonakalayo inokwenziwa khona kukwenza iimodeli ezichanekileyo nezisebenzayo ze-NLP.
Oku kunokukhokelela ekuzinzisweni okuphuculweyo kwevektha ethi ibambe intsingiselo kunye nomxholo wesicatshulwa ngokuchanekileyo ngakumbi, nto leyo eyenza ukhangelo luchaneke ngakumbi kwaye lufaneleke.
Omnye ummandla wokuqhubela phambili unokuba zii-algorithms ezihambele phambili ngakumbi zokuma kunye neenjini zokucebisa, ezivumela iingcebiso ezilungiselelwe ngakumbi nezijoliswe kuzo.
Ngaphaya koko, inkqubela phambili yetekhnoloji, efana nee-GPUs kunye nee-CPU ezikhethekileyo, inokunceda ukwandisa isantya kunye nokusebenza kakuhle kwedatha yedatha yeVector. Ngale ndlela banokufikeleleka ngakumbi kwiintlobo ezahlukeneyo zabasebenzisi kunye nezicelo.
Shiya iMpendulo