Ziziphi ii-database ze-vector, zisebenza njani kunye neemarike ezinokubakho

I-database ye-vector luhlobo lwedatha egcina idatha njenge-high-dimensional vectors, ezibonisa imathematika yeempawu okanye iimpawu. 

Ezi vectors zihlala ziveliswa ngokufaka uhlobo oluthile lomsebenzi wokuzinzisa kwidatha ekrwada, efana nesicatshulwa, imifanekiso, iaudio, ividiyo, kunye nezinye.

Oovimba beenkcukacha zeVector banokuba definited njengesixhobo esibonisa kwaye sigcina izigxina zevektha zokufumana ngokukhawuleza kunye nokufana nokufuna, kunye neempawu ezifana nokucoca i-metadata kunye nokukalwa okuthe tye.

Ixesha lokufunda eliqikelelweyo: 9 imizuzu

Ukukhula komdla wabatyali mali

Kwiiveki zamva nje, kuye kwakho ukonyuka komdla womtyali-mali kwiziko ledatha ye-vector. Ukusukela ekuqaleni kuka-2023 siye saqaphela ukuba:

• ukuqaliswa kwesiseko sedatha yevector Weaviate Wafumana I-50 yezigidi zeedola kwinkxaso-mali ye-Series B;
• IPinecone inyuse i-100 yezigidi zeerandi kwinkxaso-mali ye-Series B ngexabiso le-750 yezigidi zeedola;
• Chroma , iprojekthi yomthombo ovulekileyo, inyuse i-18 yezigidi zeerandi kwi-database yayo yokufakela;

Makhe sibone ngokubanzi ukuba zeziphi ii-database ze-vector.

IiVectors njengomelo lwedatha

Ugcino lwedatha yeVector luxhomekeke kakhulu kwi-vector embeding, uhlobo lokubonakaliswa kwedatha ethwala ngaphakathi kuyo ulwazi lwe-semantic olubalulekileyo kwi-AI ukuze ifumane ukuqonda kunye nokugcina inkumbulo yexesha elide yokutsala xa isenza imisebenzi enzima. 

Ukuzinzisa iVector

I-Vector embeds ifana nemephu, kodwa endaweni yokusibonisa apho izinto zikhoyo emhlabeni, zisibonisa apho izinto zikhoyo kwinto ebizwa. indawo yevektha. Indawo yeVector luhlobo lwebala lokudlala elikhulu apho yonke into inendawo yayo yokudlala. Khawucinge ukuba uneqela lezilwanyana: ikati, inja, intaka kunye nentlanzi. Sinokwenza i-vector embed kumfanekiso ngamnye ngokuwunika indawo ekhethekileyo kwindawo yokudlala. Ikati inokuba kwikona enye, inja kwelinye icala. Intaka inokuba sesibhakabhakeni kwaye intlanzi inokuba sechibini. Le ndawo yindawo ene-multidimensional. Umlinganiselo ngamnye uhambelana nemiba eyahlukeneyo yabo, umzekelo, intlanzi inamaphiko, iintaka zinamaphiko, iikati kunye nezinja zinemilenze. Enye inkalo yazo isenokuba yeyokuba iintlanzi zezamanzi, iintaka ubukhulu becala zezesibhakabhaka, iikati nezinja phantsi. Sakuba sineevekhtha, sinokusebenzisa ubuchule bemathematika ukuzibeka ngokwamaqela ngokusekwe kukufana kwazo. Ngokusekelwe kulwazi esinalo,

Ngoko ke, ukufakwa kwe-vector kufana nemephu esinceda sifumane ukufana phakathi kwezinto ezikwindawo ye-vector. Kanye njengokuba imaphu isinceda ukuba sijonge ilizwe, i-vector embed iyanceda ukukhangela indawo yokudlala.

Ingcinga ephambili yeyokuba izigxina ezizinzisayo ziyafana enye kwenye inomgama omncinci phakathi kwazo. Ukufumanisa ukuba zifana kangakanani na, sinokusebenzisa imisebenzi yomgama we-vector efana nomgama we-Euclidean, umgama we-cosine, njl.

Ugcino lwedatha yeVector vs amathala eencwadi

Iilayibrari zeVector gcina uzinziso lwe vectors kwizalathisi kwinkumbulo, ukuze wenze uphendlo olufanayo. Amathala eencwadi eVector anezi mpawu/izithintelo zilandelayo:

1. Gcina iivektha kuphela : Amathala eencwadi eeVektha agcina kuphela izinto ezizinzisiweyo zeevekhtha hayi izinto ezinxulumeneyo eziye zenziwa ngazo. Oku kuthetha ukuba xa sibuza, ilayibrari ye-vector iya kuphendula ngeevektha ezifanelekileyo kunye ne-ID yento. Oku kuthintelwa kuba olona lwazi lugcinwe kwinto hayi id. Ukusombulula le ngxaki, kufuneka sigcine izinto kwindawo yokugcina yesibini. Singasebenzisa ke ii-ID ezibuyiselwe ngumbuzo kwaye sizidibanise nezinto ukuqonda iziphumo.
2. Idatha yesalathisi ayinakuguqulwa : Izalathisi eziveliswe ngamathala eencwadi aziguquki. Oku kuthetha ukuba xa sele singenise idatha yethu kwaye sakha isalathiso, asikwazi ukwenza naluphi na utshintsho (akukho kufakelo okutsha, ukucima, okanye utshintsho). Ukwenza utshintsho kwisalathiso sethu, kuya kufuneka sisakhe kwakhona ukusuka ekuqaleni
3. Umbuzo ngelixa unciphisa ukuthathwa ngaphandle : Iilayibrari ezininzi zevekhtha azinakubuzwa xa kuthathwa ngaphandle idatha. Kufuneka singenise zonke izinto zethu zedatha kuqala. Ngoko isalathisi senziwe emva kokuba izinto zingeniswa ngaphandle. Oku kunokuba yingxaki kwizicelo ezifuna izigidi okanye iibhiliyoni zezinto ukuba zingeniswe ngaphandle.

Kukho iilayibrari ezininzi zokukhangela i-vector ezikhoyo: FAISS kaFacebook, uyacaphukisa nguSpotify kunye IskenaNN nguGoogle. I-FAISS isebenzisa indlela yokudibanisa, i-Annoy isebenzisa imithi kunye ne-ScanNN isebenzisa i-vector compression. Kukho utshintshiselwano lwentsebenzo nganye, esinokuyikhetha ngokusekelwe kwisicelo sethu kunye neemetriki zokusebenza.

CRUD

Into ephambili eyahlula i-database ye-vector kwiilayibrari ze-vector kukukwazi ukugcina, ukuhlaziya kunye nokucima idatha. Oovimba beenkcukacha zeVector banenkxaso ye-CRUD gqiba (yenza, funda, uhlaziye kwaye ucime) esombulula imida yelayibrari ye-vector.

1. Gcina iivektha kunye nezinto : Oovimba bedatha banokugcina zombini izinto zedatha kunye ne-vectors. Ekubeni zombini zigciniwe, sinokudibanisa uphendlo lwevektha kunye nezihluzo ezicwangcisiweyo. Izihluzi zisivumela ukuba siqinisekise ukuba abona bamelwane basondeleyo bayahambelana nesihluzo semetadata.
2. Ukuguquguquka : njengogcino lwedatha oluxhasa ngokupheleleyo crud, singongeza ngokulula, sisuse okanye sihlaziye amangeniso kwisalathiso sethu emva kokuba senziwe. Oku kuluncedo ngakumbi xa usebenza ngedatha eguqukayo rhoqo.
3. Uphendlo lwexesha lokwenyani : Ngokungafaniyo namathala eencwadi e-vector, i-database iyasivumela ukuba sibuze kwaye silungise idatha yethu ngexesha lenkqubo yokungenisa. Njengoko silayisha izigidi zezinto, idatha engenisiweyo ihlala ifikeleleka ngokupheleleyo kwaye isebenza, ke akufuneki ulinde ukungeniswa ngaphandle kugqibe ukuqalisa ukusebenza kwinto esele ikhona.

Ngamafutshane, i-database ye-vector ibonelela ngesisombululo esiphezulu sokusingatha i-vector embeds ngokujongana nemida yee-indices ze-vector ezizimeleyo njengoko kuxoxwe ngazo kumanqaku angaphambili.

Kodwa yintoni eyenza i-database ye-vector ibe phezulu kunezogcino lwemveli?

Ugcino lwedatha yeVector ngokuchasene noovimba bemveli

Ugcino-lwazi lwemveli luyilelwe ukugcina nokubuyisela idatha ecwangcisiweyo kusetyenziswa imifuziselo yonxulumano, nto leyo ethetha ukuba ilungiselelwe imibuzo esekelwe kwimiqolo kunye nemigca yedatha. Ngelixa kunokwenzeka ukugcina ukufakwa kwe-vector kwiziko ledatha lemveli, ezi nkcukacha azilungiselelwanga imisebenzi ye-vector kwaye ayikwazi ukwenza uphando olufanayo okanye eminye imisebenzi enzima kwiiseti zedatha ezinkulu ngokufanelekileyo.

Oku kungenxa yokuba oovimba bedatha bemveli basebenzisa ubuchule bokwenza isalathisi esekwe kwiintlobo zedatha ezilula, ezinje ngeentambo okanye amanani. Obu buchule bokwenza isalathisi abufanelekanga kwidatha yevektha, enobukhulu obuphezulu kwaye ifuna ubuchule obukhethekileyo besalathiso njengezalathisi ezijijekileyo okanye imithi yendawo.

Kwakhona, oovimba beenkcukacha bemveli abenzelwanga ukuphatha izixa ezikhulu zedatha engacwangciswanga okanye eyakhiwe ngokwesiqingatha ehlala inxulunyaniswa nezingiso zevektha. Ngokomzekelo, umfanekiso okanye ifayile yeaudio inokuqulatha izigidi zamanqaku edatha, apho i-database yendabuko ayikwazi ukuphatha ngokufanelekileyo.

Ugcino lwedatha yeVektha, kwelinye icala, yenzelwe ngokukodwa ukugcina nokubuyisela idatha yevektha kwaye ilungiselelwe ukukhangela okufanayo kunye neminye imisebenzi enzima kwiiseti ezinkulu zedatha. Basebenzisa ubuchule obukhethekileyo besalathiso kunye ne-algorithms eyilelwe ukusebenza ngedatha enomgangatho ophezulu, ibenza basebenze ngakumbi kunogcino-lwazi lwemveli lokugcina kunye nokubuyisela i-vector embeds.

Ngoku ekubeni ufunde kakhulu malunga nogcino lwedatha yevektha, unokuba uyazibuza, zisebenza njani? Makhe sijonge.

Ingaba i-database ye-vector isebenza njani?

Sonke siyazi ukuba i-database yobudlelwane isebenza njani: bagcina iintambo, amanani, kunye nolunye uhlobo lwedatha ye-scalar kwimiqolo kunye neekholamu. Kwelinye icala, i-database ye-vector isebenza kwii-vectors, ngoko ke indlela ephuculwe ngayo kwaye ibuzwa yahluke kakhulu.

Kuvimba weenkcukacha zemveli, sikholisa ukubuza kwimiqolo ekwisiseko sedatha apho ixabiso lidla ngokudibana ngqo nokubuza kwethu. Kuvimba wedatha yeVector, sisebenzisa i-metric efanayo ukufumana i-vector efana kakhulu nombuzo wethu.

I-database ye-vector isebenzisa indibaniselwano ye-algorithms ezininzi ezithatha inxaxheba ekukhangelweni kommelwane okufutshane (ANN). Ezi migaqo-nkqubo zikhulisa uphendlo nge-hashing, i-quantization okanye uphendlo olusekwe kwigrafu.

Ezi algorithms zihlanganiswe zibe ngumbhobho obonelela ngokukhawuleza nangokuchanekileyo ukufunyanwa kwe-vector ebuzwayo abamelwane. Ekubeni i-database ye-vector ibonelela ngeziphumo eziqikelelweyo, urhwebo oluphambili esiluqwalaselayo luphakathi kokuchaneka kunye nesantya. Okukhona ichaneke ngakumbi isiphumo, kokukhona umbuzo uyacotha. Nangona kunjalo, inkqubo elungileyo inokubonelela ngophendlo olukhawulezayo ngokuchaneka ngokugqibeleleyo.

• Isalathiso : I-database ye-vector izalathisa i-vectors isebenzisa i-algorithm efana ne-PQ, LSH okanye i-HNSW. Eli nyathelo linxulumanisa i-vectors kunye nesakhiwo sedatha esiza kuvumela ukukhangela ngokukhawuleza.
• Umbuzo : idatabase yeVektha ithelekisa umbuzo onesalathiso wevektha ngokuchasene nevektha ezinesalathiso kwiseti yedatha ukufumana abamelwane abasondeleyo (usebenzisa imetric yokuyelela esetyenziswa sesisalathiso)
• Emva kokulungiswa : Kwezinye iimeko, i-database ye-vector ilanda abamelwane abakufutshane bokugqibela kwi-dataset kwaye isebenze emva kokubuyisela iziphumo zokugqibela. Eli nyathelo lisenokuquka ukuhlelwa ngokutsha abona bamelwane basondeleyo kusetyenziswa umlinganiselo wokufana owahlukileyo.

benefits

Ugcino lwedatha yeVector sisixhobo esinamandla sokukhangela okufanayo kunye neminye imisebenzi entsonkothileyo kwiiseti zedatha enkulu, engenakwenziwa ngokufanelekileyo kusetyenziswa ugcino-lwazi lwemveli. Ukwakha i-database ye-vector esebenzayo, i-embeds ibalulekile, njengoko ibamba intsingiselo ye-semantic yedatha kwaye yenza uphando oluchanekileyo oluchanekileyo. Ngokungafaniyo namathala eencwadi eVector, idatabase yeVector iyilelwe ukuba ilingane kwimeko yethu yokusetyenziswa, ibenza ukuba balungele usetyenziso apho ukusebenza kunye nokulinganisa kubaluleke kakhulu. Ngokunyuka kokufundwa komatshini kunye nobukrelekrele bokwenziwa, i-database ye-vector iya ibaluleke kakhulu kwiintlobo ezahlukeneyo zezicelo ezibandakanya iinkqubo zokuncoma, ukukhangela umfanekiso, ukufana kwe-semantic, kunye noluhlu luyaqhubeka. Njengoko ibala liqhubeka nokuvela, sinokulindela ukubona izicelo ezintsha zogcino lwedatha kwikamva.

Ercole Palmeri