I-Artificial intelligence (AI) ishintsha indlela esicubungula nesiyihlola ngayo idatha. Futhi, imininingwane egciniwe ye-vector ingelinye lamathuluzi ayinhloko ashayela lolu shintsho.
Lezi zingosi zolwazi zisebenza kahle kakhulu ekugcineni nasekubuyiseni izethulo zedatha ezinobukhulu obuphezulu.
Banamandla okudlala indima ebalulekile empumelelweni yezinhlelo zokusebenza ze-AI njengokucutshungulwa kolimi lwemvelo, ukuqashelwa kwezithombe, nezinhlelo zokuncoma.
Kulokhu okuthunyelwe, sizobheka inkambu ethokozisayo yolwazi lwe-vector ku-AI nokuthi kungani ibaluleke kangaka kososayensi bedatha nochwepheshe bokufunda ngomshini.
Kungani Imininingo Egciniwe Yezobudlelwano Ingenele Izicelo ze-AI
Sivamise ukugcina futhi sibuyise idatha sisebenzisa imininingwane egciniwe yobudlelwano evamile. Kodwa-ke, lezi zingosi zolwazi azihlali zikulungele kahle izethulo zedatha ezinobukhulu obuphezulu, okuyisidingo esivamile ezinhlelweni eziningi ze-AI.
Ukucubungula amanani amakhulu edatha engahlelekile evame ukusetshenziswa ku-AI kungaba inselele ngenxa yemvelo ehlelekile yalezi sizindalwazi.
Ochwepheshe bebefuna ukugwema ukusesha okubambezelekile nokungasebenzi. Ngakho-ke, ukuze banqobe lezi zinselele, basebenzise izixazululo ezinjengokwenza isicaba izakhiwo zedatha. Nokho, lena bekuyinqubo edla isikhathi futhi ethanda ukuba namaphutha.
Indlela esebenza ngempumelelo kakhulu yokugcina kanye nokubuyisa idatha enobukhulu obuphezulu iye yavela ngokukhula kolwazi lwe-vector. Ngale ndlela, kungenzeka ukuthi kube nezinhlelo zokusebenza ze-AI ezilula futhi eziphumelelayo.
Manje, ake sibone ukuthi lezi sizindalwazi se-vector zisebenza kanjani.
Iyini ngempela imininingwane egciniwe ye-vector?
Imininingo egciniwe yamaVektha iyisizindalwazi esikhethekile esihloselwe ukugcina nokusingatha amanani amakhulu edatha enobukhulu obuphezulu ngendlela yama-vector.
Ama-Vector ayizethulo zedatha yezibalo ezichaza izinto ngokusekelwe kuzici zazo ezihlukene noma izimfanelo.
Ivekhtha ngayinye imelela iphuzu ledatha elilodwa, njengegama noma isithombe, futhi yakhiwe iqoqo lamanani elichaza izimfanelo zayo eziningi. Lezi zinhlobonhlobo ngezinye izikhathi zaziwa ngokuthi "izici" noma "ubukhulu."
Isithombe, isibonelo, singase simelwe njengevekhtha yamanani amaphikseli, kodwa umusho wonke ungase umelwe njengevekhtha yokushumeka kwamagama.
Imininingo egciniwe yamaVekhtha isebenzisa amasu okukhomba ukuze kube lula ukutholakala kwama-vector afana nevekhtha yombuzo othile. Lokhu kunenzuzo ikakhulukazi ku ukufunda imishini izinhlelo zokusebenza, njengoba ukusesha okufanayo kuvame ukusetshenziswa ukuthola amaphuzu edatha aqhathanisekayo noma ukukhiqiza iziphakamiso.
Ukusebenza Kwangaphakathi Kwemininingwane Yedatha YeVector
Imininingo egciniwe yeVector isetshenziselwa ukugcina kanye nokukhomba amavekhtha anobukhulu obuphezulu akhiqizwa amasu afana nala ukufunda okujulile. Lawa mavekhtha ayizethulo zezinombolo zezinto zedatha eziyinkimbinkimbi ezihunyushwa esikhaleni esinohlangothi oluphansi kuyilapho kugcinwa ulwazi olubalulekile kusetshenziswa indlela yokushumeka.
Ngakho-ke, imininingwane egciniwe yamavekhtha yakhelwe ukubhekelela ukwakheka okuthile kokushumekwa kwe-vector, futhi isebenzisa ama-algorithms wokukhomba ukuze iseshe ngempumelelo futhi ithole ama-vector asuselwa ekufaneni kwawo nevekhtha yombuzo.
Isebenza kanjani?
Isizindalwazi seVector sisebenza ngendlela efanayo namabhokisi omlingo agcina futhi ahlela izinto zedatha eziyinkimbinkimbi.
Basebenzisa izindlela ze-PQ ne-HNSW ukuze bakhombe futhi bathole ulwazi olufanele ngokushesha. I-PQ isebenza ngendlela efanayo nesitini se-Lego, ifinyeza ama-vector abe izingxenye ezincane ukusiza ekusesheni okuqhathanisekayo.
I-HNSW, ngakolunye uhlangothi, ithuthukisa iwebhu yezixhumanisi ukuze ihlele ama-vector ngohlelo, okwenza ukuzulazula nokusesha kube lula. Ezinye izinketho zokudala, njengokwengeza kanye nokukhipha ama-vector ukuze kutholwe ukufana nokwehluka, nazo zisekelwa isizindalwazi se-vector.
I-Vector Database isetshenziswa kanjani ku-AI?
Imininingo egciniwe yeVector inamandla amakhulu endaweni ye ukuhlakanipha okungekhona okwangempela. Zisisiza ukuthi silawule inani elikhulu ledatha futhi zisekele imisebenzi eyinkimbinkimbi njengosesho lokufana kanye ne-vector arithmetic.
Seziphenduke amathuluzi abalulekile ezinhlobonhlobo zezinhlelo zokusebenza. Lokhu kufaka phakathi ukucutshungulwa kolimi lwemvelo, ukubonwa kwezithombe, nezinhlelo zokuncoma. Ukushumeka kweVector, ngokwesibonelo, kusetshenziswa ekucubunguleni ulimi lwemvelo ukuze kubambe incazelo nomongo wombhalo, okuvumela imiphumela yokusesha enembile nefanele.
Imininingo egciniwe yeVekhtha ekubonweni kwesithombe ingasesha izithombe eziqhathanisekayo kahle, ngisho nakumadathasethi amakhulu. Bangaphinde banikeze izinto eziqhathanisekayo noma ulwazi kumakhasimende ngokusekelwe kulokho abakuthandayo nokuziphatha ezinhlelweni zokuncoma.
Imikhuba Engcono Kakhulu Yokusebenzisa Imithombo Yedatha YeVector Kubuhlakani Obunziwe
Ukuze kuqalwe, ama-vectors okufakwayo kufanele acutshungulwe ngaphambili futhi enziwe abejwayelekile ngaphambi kokugcinwa kusizindalwazi. Lokhu kungakhuphula ukunemba nokusebenza kosesho lwe-vector.
Okwesibili, i-algorithm yenkomba efanele kufanele ikhethwe kuye ngesimo sokusetshenziswa komuntu ngamunye kanye nokusatshalaliswa kwedatha. ama-algorithms ahlukahlukene anokuhweba okuhlukene phakathi kokunemba nesivinini, futhi ukukhetha efanele kungaba nomthelela omkhulu ekusebenzeni kokusesha.
Okwesithathu, ukuze kuqinisekiswe ukusebenza kahle, isizindalwazi se-vector kufanele sigadwe futhi sigcinwe njalo. Lokhu kuhlanganisa ukukhomba kabusha isizindalwazi njengoba kudingeka, ukulungisa kahle imingcele yokukhomba, nokuqapha ukusebenza kosesho ukuze kutholwe futhi kuxazululwe noma yiziphi izinkinga.
Okokugcina, ukukhulisa amandla ezinhlelo zokusebenza ze-AI, kuyalulekwa ukuthi kuqashwe isizindalwazi se-vector esisekela izici eziyinkimbinkimbi ezifana ne-vector arithmetic nokusesha okufanayo.
Kungani Kufanele Usebenzise Isizindalwazi SeVector?
Inhloso ejwayelekile kakhulu yokusebenzisa isizindalwazi se-vector wusesho lwe-vector ekukhiqizeni. Ukufana kwezinto eziningi nombuzo wosesho noma into yesihloko kuqhathaniswa ngale ndlela yokusesha. Isizindalwazi se-vector sinamandla okuqhathanisa ukufana kwalezi zinto ukuze kutholwe okufanayo okuseduze ngokuguqula isihloko noma umbuzo ube ivekhtha kusetshenziswa imodeli yokushumeka ye-ML efanayo.
Lokhu kukhiqiza imiphumela enembile ngenkathi kugwema imiphumela engabalulekile ekhiqizwe ubuchwepheshe bokusesha obujwayelekile.
Isithombe, Umsindo, Ukusesha Okufanayo kwevidiyo
Izithombe, umculo, ividiyo, nolunye ulwazi olungahlelekile kungaba nzima ukuhlukanisa nokugcina kusizindalwazi esivamile. Imininingo egciniwe yeVector iyimpendulo enhle kakhulu yalokhu njengoba ingasesha izinto eziqhathanisekayo ngokushesha ngisho nakumadathasethi amakhulu. Le ndlela ayidingi muntu ukumaka idatha noma ukulebula futhi ingathola ngokushesha okufanayo okuseduze ngokusekelwe kumaphuzu afanayo.
Izinjini Zezinga Nezincomo
Imininingo egciniwe yeVector nayo ifaneleka kahle ukuthi isetshenziswe ezinhlelweni zokukala kanye nezincomo. Angasetshenziselwa ukuncoma izinto eziqhathaniseka nokuthengwa kwangaphambilini noma into yamanje umthengi ayibhekile.
Kunokuncika ekuhlungeni okuhlanganyelwe noma kuhlu lokuduma, izinsiza zemidiya ezisakazwayo zingasebenzisa izilinganiso zengoma yomsebenzisi ukuze zinikeze iziphakamiso ezifanelana ngokuphelele ezenziwe zaba ngezakho kumuntu ngamunye. Bangakwazi ukuthola imikhiqizo eqhathanisekayo ngokusekelwe okufanayo okuseduze.
Usesho lwe-Semantic
Usesho lwe-Semantic luyithuluzi eliqinile lokusesha umbhalo nedokhumenti eleqa ukusesha kwamagama angukhiye ajwayelekile. Incazelo nomongo weyunithi yezinhlamvu zombhalo, imishwana, nawo wonke amadokhumenti kungaqondwa ngokusebenzisa isizindalwazi se-vector ukuze sigcine futhi sikhombise ukushumeka kwevekhtha evela ku-Natural. Amamodeli Wokucubungula Ulimi.
Ngakho, abasebenzisi bazokwazi ukuthola abakudingayo ngokushesha ngaphandle kokuqonda ukuthi idatha ihlukaniswa kanjani.
Ubuchwepheshe Bama-Vector Database
Kunobuchwepheshe obuhlukahlukene be-vector database obutholakalayo, ngayinye inesethi yayo yezinzuzo kanye nokubi.
I-Pinecone, Faiss, Iyacasula, Milvus, Futhi Hnswlib amanye amathuba adume kakhulu.
I-Pinecone
Kuyisizindalwazi se-vector esekwe emafini. Ungathuthukisa izinhlelo zokusebenza zokucinga ezifanayo ngesikhathi sangempela. Ivumela abasebenzisi ukuthi bagcine futhi bahlole ukushumekwa kwevekhtha enobukhulu obuphezulu nge-millisecond latencies.
Lokhu kuyenza ifaneleke izinhlelo zokusebenza ezifana nezinhlelo zokuncoma, ukusesha izithombe nevidiyo, kanye nokucutshungulwa kolimi lwemvelo.
Izici eziyinhloko ze-Pinecone zifaka ukukhomba okuzenzakalelayo, izibuyekezo zesikhathi sangempela, ukushuna okuzenzakalelayo kombuzo, kanye ne-REST API yokusebenzelana okulula nezinqubo zamanje. Izakhiwo zayo zakhelwe ukuqina nokuqina. Ungakwazi ukuphatha kalula amanani amakhulu edatha ngenkathi ugcina ukutholakala okuphezulu.
Faiss
Kuyiphakheji ye-Facebook yomthombo ovulekile ehlinzeka ngokusetshenziswa okuphambili kokukhomba nokusesha ama-algorithms wama-vector amakhulu.
Isekela amasu amaningana okusesha ama-vector. Enye yezinzuzo zayo eziyinhloko ijubane nokukaleka kwayo, okuvumela ukusesha okusheshayo ngisho nakumadathasethi anezigidigidi zama-vector.
Iyacasula
I-Annoy, ngakolunye uhlangothi, ilabhulali ye-C++ eyakhelwe ukusesha komakhelwane okucishe kube sezingeni eliphezulu. Kulula ukuyisebenzisa futhi isebenzise inqubo yesihlahla esingahleliwe ngokushesha.
I-Annoy ilabhulali yenkumbulo encane efanelekile ukuthi isetshenziswe ezimeni ezicindezelwe yizinsiza.
Milvus
I-Milvus isizindalwazi se-vector samahhala nesivulekile sokugcina nokusesha ama-vector amakhulu. Isekela amasu okukhomba ahlukahlukene, okuhlanganisa i-IVF ne-HNSW, futhi ingaphatha kalula izigidi zamavekhtha.
Amandla ayo okusheshisa kwe-GPU, okungase kusheshise kakhulu inqubo yokusesha, kungenye yezici zayo ezihluke kakhulu.
Kulula ukukhetha okungcono kakhulu lapho unquma ukukhetha umkhiqizo wesizindalwazi se-vector.
Hnswlib
I-Hnswlib ingomunye umtapo wolwazi womthombo ovulekile ohlinzeka ngenethiwekhi yomhlaba omncane okwazi ukuzulazula ukuze ukhombe ngokushesha futhi useshe ama-vector anobukhulu obuphezulu.
Kuhle kakhulu ezimweni lapho isikhala se-vector sishintsha njalo, futhi sinikeza inkomba ekhuphukayo ukuze ugcine inkomba isesimweni samanje ngamavekhtha amasha. Futhi iyalungiseka ngokwedlulele, ivumela abasebenzisi ukuthi bashune kahle ibhalansi yokunemba nesivinini.
Ama-Drawbacks angenzeka
Ngenkathi imininingwane egciniwe yama-vector inezinzuzo eziningi, futhi inobubi obubalulekile. Okunye okukhathazayo okungaba khona inani eliphezulu lesitoreji esidingekayo ukuze ulawule ukushumeka kwe-vector.
Ngaphezu kwalokho, isizindalwazi se-vector singase sibe nzima nezinhlobo ezithile zedatha, njengemibuzo emifushane noma ekhetheke kakhulu. Okokugcina, ukusetha nokuthuthukisa lezi zingosi zolwazi kungase kuhlanganise ikhono elikhulu, okuzenza zingafinyeleleki kalula kwabanye abasebenzisi.
Iyini i-The Next Level?
Kunezithuthukisi ezihlukahlukene ezingaba khona emkhathizwe njengoba isizindalwazi se-vector siqhubeka nokuvela. Indawo eyodwa lapho kungenziwa khona inqubekelaphambili enkulu ekudalweni kwamamodeli e-NLP anembe kakhudlwana nasebenza kahle.
Lokhu kungase kuholele ekushumekeni kwevekhtha okuthuthukisiwe othwebula incazelo nomongo wombhalo ngokunembe kakhudlwana, okwenza ukusesha kube nembe nakakhulu futhi kuhambisane.
Enye indawo yentuthuko ingase ibe ama-algorithms athuthuke kakhulu okulinganisa nezinjini zokuncoma, okuvumela izincomo eziklanyelwe kakhulu neziqondiswe nakakhulu.
Ngaphezu kwalokho, ukuthuthuka kwezobuchwepheshe, njengama-GPU nama-CPU akhethekile, kungasiza ukukhulisa isivinini nokusebenza kahle kwedatha yedatha ye-vector. Ngale ndlela bangafinyeleleka kalula kubasebenzisi abahlukahlukene kanye nezinhlelo zokusebenza.
shiya impendulo