Ugcino lwedatha yeVector lubonisa utshintsho olubalulekileyo kwindlela esilawula kwaye sitolika ngayo idatha, ngakumbi kwiinkalo zobukrelekrele bokwenziwa kunye nokufunda koomatshini.
Umsebenzi osisiseko wolu gcino lwedatha kukuphatha ngokufanelekileyo iivektha ezinomgangatho ophezulu, eziyimathiriyeli ekrwada yeemodeli zokufunda zoomatshini kwaye zibandakanya ukuguqulwa kombhalo, umfanekiso, okanye igalelo leaudio kumelo lwamanani kwindawo ene-multidimensional.
Kwizicelo ezinje ngeenkqubo zokucebisa, ukuqondwa kwento, ukufunyanwa kwemifanekiso, kunye nokubhaqwa kobuqhophololo, olu tshintsho lungaphezulu nje kokugcinwa; ngumnyango wesakhono esinamandla kukhangelo olufanayo kunye nemibuzo ekufutshane-yabamelwane.
Ngokunzulu ngakumbi, amandla ogcino-lwazi lwe-vector akwisikhundla sawo sokuguqulela inani elikhulu ledatha engacwangciswanga, entsonkothileyo kwii-vectors ezibamba umxholo kunye nentsingiselo yomxholo wokuqala.
Imisebenzi yokukhangela eyomeleziweyo eyenziwe yenzeke ngokufakela imifuziselo kolu khowudo iquka ukukwazi ukubuza iivektha ezingqonge ukufumana imifanekiso okanye amabinzana ayeleleneyo.
Ugcino lwedatha yeVector lukhethekile kuba lwakhiwe kubuchule obuphambili besalathiso njengeInverted File Index (IVF) kunye neHierarchical Navigable Small World (HNSW), ephucula isantya sabo kunye nokusebenza kakuhle ngelixa befumana abamelwane abakufutshane kwizithuba ze-N-dimensional.
Kukho umahluko ocacileyo phakathi kwevektha kunye nesiseko sedatha sakudala. Ugcino-lwazi oluqhelekileyo lulungile ekulungelelaniseni idatha kwiiseti ezicwangcisiweyo ezenziwe yi-CRUD-eyenziwe kakuhle kwaye ibambelela kwi-schemas.
Nangona kunjalo, xa ujongene nendalo eguqukayo kunye neyinkimbinkimbi yedatha ephezulu, oku kuqina kuqala ukuba ngumqobo.
Ngokwahlukileyo, oovimba bedatha bevektha banikezela ngenqanaba lokuguquguquka kunye nokusebenza kakuhle ukuba imilinganiselo yemveli ayinakulingana, ngakumbi kwizicelo ezithembele kakhulu kwi. yokufunda umatshini kunye nobukrelekrele bokwenziwa. Abanakwenzeka nje ukuhla kwaye banobuchule kukhangelo olufanayo.
Oovimba beenkcukacha zeVektha baluncedo kakhulu kwizicelo ze-AI ezivelisayo. Ukuqinisekisa ukuba imathiriyeli eyiliweyo igcina imfezeko yomxholo, ezi zicelo-eziquka ukusetyenzwa kolwimi lwendalo kunye nokuveliswa kwemifanekiso-zixhomekeke ekufumaneni kwakhona okukhawulezileyo kunye nothelekiso lokuzinzisa.
Ke kwesi siqwenga, siza kujonga idatabase ephezulu yevektha yeprojekthi yakho elandelayo.
1. Milvus
I-Milvus yisiseko sedatha yevektha evulelekileyo eyenzelwe ikakhulu usetyenziso lwe-AI, kubandakanywa uphendlo olufakelweyo lokufana kunye neeMLOps ezinamandla.
Iyahluka koovimba beenkcukacha abaqhelekileyo bobudlelwane, abaphatha ubukhulu becala Idatha eyakhiwe, ngenxa yesi sikhundla, esenza ukuba sikwazi ukukhomba i-vectors kwi-triliyoni engazange ibonwe ngaphambili.
Ukuzinikezela kuka-Milvus ku-scalability kunye nokufumaneka okuphezulu kubonakaliswa yindlela ephuhlise ngayo ukusuka kwinguqulelo yayo yokuqala ukuya kwi-Milvus 2.0 esasazwe ngokupheleleyo.
Ngokukodwa, i-Milvus 2.0 ibonisa uyilo olupheleleyo lwamafu olujolise ekufumanekeni okumangalisayo kwe-99.9% ngelixa ulinganisa ngaphaya kwamakhulu eendawo.
Kulabo bafuna isisombululo se-database ye-vector ethembekileyo, olu shicilelo luza lunconywa kakhulu kuba alugcini nje ukongeza iimpawu eziyinkimbinkimbi ezifana noxhulumaniso lwamafu amaninzi kunye nephaneli yokulawula, kodwa ikwaphucula amanqanaba okuhambelana kwedatha yophuhliso lwesicelo esiguquguqukayo.
Inzuzo eqaphelekayo yeMilvus yindlela yayo eqhutywa luluntu, ebonelela ngenkxaso yeelwimi ezininzi kunye nesixhobo esibanzi esilungiselelwe iimfuno zabaphuhlisi.
Kwicandelo le-IT, i-cloud scalability kunye nokuthembeka, kunye ne-high-performance vector search capabilities kwi-datasets enkulu, yenza kube yinto ethandwayo.
Ukongeza, iphucula ukusebenza kakuhle kwemisebenzi yayo isebenzisa isakhono sokukhangela esixutyiweyo esidibanisa ukukhangela okufanayo kwe-vector kunye nohluzo lwe-scalar.
UMilvus unephaneli yolawulo ecacileyo Indawo yomsebenzisi, Iseti epheleleyo yee-APIs, kunye ne-scalable and tunable architecture.
Unxibelelwano kunye nezicelo zangaphandle luququzelelwa ngumgangatho wokufikelela, ngelixa ukulinganisa umthwalo kunye nokulawulwa kwedatha kulungelelaniswa yinkonzo yomququzeleli, osebenza njengomyalelo ophakathi.
Ukuhlala kwesiseko sedatha kuxhaswa ngumaleko wokugcinwa kwezinto, ngelixa iindawo zabasebenzi ziqhuba imisebenzi yokuqinisekisa ukulinganisa.
namaxabiso
Ikhululekile ukuba isetyenziswe kumntu wonke.
2. FAISS
Iqela loPhando le-AI likaFacebook liphuhlise ithala leencwadi elinokuphela elibizwa ngokuba yi-Facebook AI yokuFanayo yokuKhangela eyilelwe ukwenza ukudityaniswa kweevektha ezixineneyo kunye nokukhangela okufanayo kusebenze ngakumbi.
Ukuyilwa kwayo kuqhutywa yimfuneko yokuphucula ukufana kwe-Facebook AI yokukhangela amandla ngokusebenzisa iindlela ezisisiseko zokusika.
Xa kuthelekiswa nokuphunyezwa kwe-CPU-based, ukuphunyezwa kwe-GPU ye-FAAISS ye-GPU inokukhawulezisa amaxesha okukhangela ngokuphindwe kahlanu ukuya kwalishumi, okwenza kube sisixhobo esixabiseke kakhulu kwizicelo ezahlukeneyo, kubandakanywa neenkqubo zokucebisa kunye nokuchongwa kweentsingiselo ezifanayo kubukhulu obukhulu. iiseti zedatha ezingalungiswanga njengombhalo, isandi, kunye nevidiyo.
I-FAISS inokusingatha uluhlu olubanzi lweemetrikhi ezifanayo, ezifana nokufana kwe-cosine, imveliso yangaphakathi, kunye ne-L2 metric esetyenziswa rhoqo (umgama we-Euclidean).
Le milinganiselo yenza kube lula ukwenza uphando oluchanekileyo nolubhetyebhetye lokufana kuzo zonke iindidi zedatha. Iimpawu ezifana nokusetyenzwa kwebhetshi, ukurhweba ngesantya esichanekileyo, kunye nenkxaso yazo zombini ezichanekileyo kunye nophendlo oluqikelelweyo zonyusa ngakumbi ukuguquguquka kwayo.
Ukongeza, i-FAISS ibonelela ngendlela enokukala yokuphatha iiseti zedatha ezinkulu ngokuvumela izalathisi ukuba zigcinwe kwidiski.
Ifayile eguqulweyo, ubungakanani bemveliso (PQ), kunye nePQ ephuculweyo zimbalwa zeendlela ezintsha ezenza isiseko sophando se-FAISS kwaye yongeza ekusebenzeni kwayo xa kufikwa kwisalathiso kunye nokukhangela amasimi evector anomgangatho ophezulu.
Ezi zicwangciso zomelezwa ziindlela zokusika ezinje nge-GPU-i-accelerated k-selection algorithms kunye nokuhluzwa kwangaphambili kwemigama ye-PQ, iqinisekisa amandla e-FAISS ukuvelisa iziphumo ezikhawulezayo nezichanekileyo zokukhangela nakwiiseti zedatha ezikwibhiliyoni.
namaxabiso
Ikhululekile ukuba isetyenziswe kumntu wonke.
3. IPinecone
I-Pinecone yinkokeli kwi-database ye-vector, inikezela nge-cloud-native, inkonzo elawulwayo eyakhelwe ngokukodwa ukuphucula ukusebenza kwezicelo eziphezulu ze-AI.
Yenzelwe ngokukodwa ukusingatha ukufakwa kwe-vector, okuyimfuneko kwi-AI yokuvelisa, uphendlo lwesemantic, kunye nosetyenziso olusebenzisa imifuziselo yolwimi olukhulu.
I-AI ngoku inokuqonda ulwazi lwesemantic enkosi kolu fakelo, olusebenza ngokufanelekileyo njengenkumbulo yexesha elide kwimisebenzi enzima.
I-Pinecone yahlukile kuba idibanisa ngaphandle komthungo izakhono zogcino-lwazi lwemveli kunye nokusebenza okuphuculweyo kwezalathisi ze-vector, ivumela ugcino olusebenzayo nolukhulu kunye nokubuza kuzinziso.
Oku kuyenza ibe lukhetho olugqibeleleyo kwiimeko apho ubunzima kunye nomthamo wedatha obandakanyekayo unikezela ugcino-lwazi olusekwe kwi-scalar lungonelanga.
I-Pinecone inikezela abaphuhlisi isisombululo esingenangxaki ngenxa yendlela yenkonzo elawulwayo, eyenza ukudibanisa kunye neenkqubo zangempela zokuthatha idatha.
Iinkqubo ezininzi zedatha zixhaswa yiyo, kubandakanya ukulanda, ukuhlaziya, ukucima, ukubuza, kunye nokunyusa idatha.
I-Pinecone iphinda iqinisekise ukuba imibuzo emele uhlengahlengiso lwexesha lokwenyani olufana nokuphazamiseka kunye nokucima inika iimpendulo ezichanekileyo, ezisezantsi-mva kwizalathisi ezineebhiliyoni zeevektha.
Kwiimeko eziguqukayo, olu phawu lubalulekile ekugcineni ukufaneleka kunye nokutsha kweziphumo zemibuzo.
Ukongeza, intsebenziswano yePinecone kunye ne-Airbyte ngoqhagamshelwano lwePinecone kwandisa ukuguquguquka kwayo kunye nokuguquguquka, okuvumela ukuhlanganiswa kwedatha egudileyo ukusuka kuluhlu lwemithombo.
Ngobu budlelwane, iindleko kunye nokusebenza kakuhle kunokuphuculwa ngokuqinisekisa ukuba ulwazi olutsha kuphela oluphathwayo ngolungelelwaniso lwedatha.
Uyilo lwesiqhagamshelo lugxininisa ukulula, lufuna ubuncinci beeparamitha zokuseta, kwaye luyanwebeka, luvumela ukuphuculwa kwexesha elizayo.
namaxabiso
Ixabiso leprimiyamu liqala ukusuka kwi-5.80 yeedola / ngenyanga kwimeko yokusetyenziswa kwe-RAG.
4. Weaviate
I-Weaviate yi-database ye-vector ye-innovative ekhoyo njenge-software yomthombo ovulekileyo oguqula indlela yokufikelela kunye nokusebenzisa idatha.
I-Weaviate isebenzisa amandla okukhangela i-vector, eyenza uphendlo oluntsonkothileyo, oluqonda imeko kuzo zonke iiseti zedatha ezinkulu, ezintsokothileyo, ngokuchaseneyo nogcino-lwazi oluqhelekileyo oluxhomekeke kumaxabiso e-scalar kunye nemibuzo echazwe kwangaphambili.
Ngale ndlela, unokufumana umxholo ngokusekelwe kwindlela efana ngayo neminye imixholo, ephucula intuitiveness yokukhangela kunye nokufaneleka kweziphumo.
Ukuhlanganiswa kwayo okugudileyo kunye neemodeli zokufunda ngomatshini yenye yeempawu zayo eziphambili; oku kuvumela ukuba isebenze ngaphezu kwesisombululo sokugcina idatha; ikwavumela idatha ukuba iqondwe kwaye ihlalutywe kusetyenziswa ubukrelekrele bokwenziwa.
I-architecture ye-Weaviate idibanisa oku kudityaniswa ngokucokisekileyo, okwenza kube lula ukuhlalutya idatha enzima ngaphandle kokusetyenziswa kwezixhobo ezongezelelweyo.
Inkxaso yayo kwiimodeli zedatha yegrafu ikwabonelela ngembono eyahlukileyo kwidatha njengamaqumrhu aqhagamsheleneyo, iipatheni ezivezwayo kunye nokuqonda okunokuthi kuphoswe kuyilo lwesiseko sesiseko sesiqhelo.
Ngenxa yoyilo lwemodyuli ye-Weaviate, abathengi banokongeza amandla afana ne-vectorization yedatha kunye nokudalwa kogcino njengoko kufuneka.
Uguqulelo lwayo olusisiseko lusebenza njengesiseko sedatha yengcaphephe yedatha, kwaye inokwandiswa kunye nezinye iimodyuli ukuhlangabezana neemfuno ezahlukeneyo.
Ukukaleka kwayo komelezwa ngakumbi kuyilo lwemodyuli, eqinisekisa ukuba isantya asiyi kunikelwa njengedini ukuphendula ubuninzi bedatha kunye neemfuno zemibuzo.
Indlela eguquguqukayo nesebenzayo yokusebenzisana nedatha egciniweyo yenziwa ukuba ibekho yinkxaso yesiseko sedatha kuzo zombini i-RESTful kunye ne-GraphQL APIs.
Ngokukodwa, iGraphQL ikhethiwe ngenxa yomthamo wayo wokuqhuba ngokukhawuleza imibuzo entsonkothileyo, esekwe kwigrafu, evumela abasebenzisi ukuba bafumane ngokuchanekileyo idatha abayifunayo ngaphandle kokufumana idatha eninzi okanye engonelanga.
I-Weaviate isebenziseka ngakumbi kwiithala leencwadi zabathengi kunye neelwimi zokucwangcisa ngenxa ye-API yayo eguquguqukayo.
Kwabo bafuna ukuphonononga i-Weaviate ngakumbi, kukho intaphane yamaxwebhu kunye nezifundo ezifumanekayo, ukusuka ekusekweni nasekuqwalaseleni umzekelo wakho ukuya kutshona nzulu kumandla ayo anjengokukhangela i-vector, ukudibanisa umatshini wokufunda, kunye noyilo lweschema.
Ungafikelela kubuchwephesha obufanayo obunamandla okwenza ulwazi luguquguquke kwaye lusebenze nokuba uthatha isigqibo sokusebenzisa i-Weaviate ekuhlaleni, kwindawo ukhomyutha yamafu okusingqongileyo, okanye ngenkonzo yelifu elawulwayo ye-Weaviate
namaxabiso
Ixabiso leprimiyamu yeqonga liqala ukusuka kwi-25 yeedola / ngenyanga ngenxa yokungabinaseva.
5. Chroma
I-Chroma yindawo yogcino lwedatha yevektha ejolise ekuguquleni ukufunyanwa nokugcinwa kwedatha, ngakumbi kwizicelo ezibandakanya ukufunda koomatshini kunye nobukrelekrele bokwenziwa.
Ekubeni i-Chroma isebenza kunye ne-vectors endaweni yamanani e-scalar, ngokungafaniyo ne-database esemgangathweni, ilungile kakhulu ekulawuleni i-high-dimensional, idatha enzima.
Le yinkqubela phambili enkulu kwitekhnoloji yokubuyiswa kwedatha kuba yenza ukhangelo oluntsonkothileyo olusekwe kukufaniswa kwe-semantic yemathiriyeli kunokuba kuhambelana negama elingundoqo elichanekileyo.
Uphawu oluphawulekayo lweChroma kukukwazi ukusebenza kunye nezisombululo ezininzi zokugcina ezingaphantsi, ezifana ne-ClickHouse yokusetha i-scaled kunye ne-DuckDB yofakelo oluzimeleyo, isiqinisekiso sokuguquguquka kunye nokulungelelanisa kwiimeko ezahlukeneyo zokusetyenziswa.
I-Chroma yenziwe ngokulula, isantya, kunye nohlalutyo engqondweni. Iyafumaneka kuluhlu olubanzi lwabaphuhlisi abane-SDKs zePython kunye neJavaScript/TypeScript.
Ukongezelela, i-Chroma ibeka ugxininiso oluqinileyo kumsebenzisi-ubuhlobo, ukuvumela abaphuhlisi ukuba bamise ngokukhawuleza isiseko sedatha esisigxina exhaswa yi-DuckDB okanye i-database yememori yokuvavanya.
Ikhono lokwakha izinto zokuqokelela ezifana neetafile kwiindawo eziqhelekileyo zolwazi, apho idatha yombhalo ingafakwa kwaye iguqulwe ngokuzenzekelayo ibe yi-embeddings usebenzisa imizekelo efana nayo yonke i-MiniLM-L6-v2, ikwandisa ngakumbi oku kuninzi.
Umbhalo kunye nofakelo lunokudityaniswa ngokungenamthungo, okuyimfuneko kwizicelo ezifuna ukubamba iisemantics zedatha.
Isiseko sendlela yokufana yevektha yeChroma ziingqikelelo zemathematika ze-orthogonality kunye noxinaniso, eziyimfuneko ekuqondeni ukumelwa kunye nokuthelekiswa kwedatha kwiziko ledatha.
Ezi ngcamango zivumela iChroma ukuba iqhube uphando olunentsingiselo nolusebenzayo lokufana ngokuthathela ingqalelo unxibelelwano lwesemantic phakathi kwezinto zedatha.
Izixhobo ezinje ngezifundo kunye nezikhokelo ziyafikeleleka kubantu abafuna ukuphonononga iChroma ngakumbi. Zibandakanya isikhokelo senyathelo ngenyathelo malunga nendlela yokuseta isiseko sedatha, ukudala ukuqokelela, kunye nokukhangela okufanayo.
namaxabiso
Ungaqala ukuyisebenzisa simahla.
6. IVespa
I-Vespa iqonga eliguqula ukuphathwa kwe-intanethi ye-AI kunye nedatha enkulu.
Injongo esisiseko yeVespa kukuvumela ukubalwa kwe-low-latency computations kuzo zonke iiseti zedatha ezinkulu, ekuvumela ukuba ugcine ngokulula, isalathisi, kunye nokuhlalutya okubhaliweyo, i-vector, kunye nedatha eyakhiweyo.
I-Vespa iyahlukaniswa ngamandla ayo okubonelela ngeempendulo ezikhawulezayo nakweliphi na isikali, kungakhathaliseki ukuba luhlobo luni lwemibuzo, ukhetho, okanye imodeli efundwa ngumatshini ephathwayo.
Ukuguquguquka kweVespa kuboniswa kwi-injini yokukhangela esebenza ngokupheleleyo kunye nedatha ye-vector, eyenza uphando oluninzi ngaphakathi kombuzo omnye, ukusuka kwi-vector (ANN), lexical, kunye nedatha ehleliweyo.
Nokuba ungakanani na, unokwenza usetyenziso olusebenziseka lula kunye noluphendulayo lokukhangela olunexesha lokwenyani le-AI enkosi ngokudityaniswa kwemodeli efundiweyo yomatshini kunye nedatha yakho.
Noko ke, iVespa ingaphezu kokufuna nje; imalunga nokuqonda kunye nokwenza ngokwezifiso ukudibana.
Izixhobo eziphezulu zokwenza ngokwezifiso kunye nengcebiso zibonelela ngeengcebiso eziguqukayo, zangoku ezilungiselelwe abasebenzisi abathile okanye iimeko.
I-Vespa ngumtshintshi-mdlalo kuye nabani na ojonge ukungena kwindawo ye-AI yencoko ngokunjalo, kuba ibonelela ngesiseko esifunekayo sokugcina kunye nokuphonononga idatha yesicatshulwa kunye ne-vector ngexesha langempela, evumela ukuphuhliswa kwee-agent ze-AI ezingaphezulu kunye nezisebenzayo.
Ngophawu olubanzi kunye ne-stemming, ukukhangela okubhaliweyo okugcweleyo, uphendlo lwabamelwane abasondeleyo, kunye nemibuzo yedatha ecwangcisiweyo zonke zixhaswa ngumbuzo obanzi weqonga.
Iyahluka kuba inokusingatha ngempumelelo imibuzo enzima ngokudibanisa imilinganiselo emininzi yokukhangela.
I-Vespa yindawo yamandla yokubala ye-AI kunye nokusetyenziswa komatshini wokufunda ngenxa yokuba injini yayo yokubala inokusingatha iintetho zemathematika ezintsonkothileyo kwi-scalars kunye ne-tensor.
Ukusebenza, iVespa yenziwe ukuba ibe lula ukuyisebenzisa kwaye yandise.
Ihlaziya iinkqubo eziphindaphindayo, ukusuka ekucwangcisweni kwenkqubo kunye nophuhliso lwesicelo ukuya kwidatha kunye nolawulo lwe-node, eyenza imisebenzi yokuvelisa ekhuselekileyo nengenakuphazamiseka.
Uyilo lweVespa luqinisekisa ukuba luyanda kunye nedatha yakho, igcina ukuxhomekeka kwayo kunye nokusebenza kwayo.
namaxabiso
Ungaqala ukuyisebenzisa simahla.
7. Quadrant
I-Qdrant yiplatifomu yedatha ye-vector eguquguqukayo ebonelela ngeseti eyodwa yezakhono ukuhlangabezana neemfuno ezikhulayo ze-AI kunye nezicelo zokufunda ngomatshini.
Kwisiseko sayo, i-Qdrant iyinjini yokukhangela efana ne-vector ebonelela nge-API ekulula ukuyisebenzisa yokugcina, ukufumana, nokugcina ii-vectors kunye nedatha yokuhlawula.
Olu phawu lubalulekile kwizicelo ezininzi, ezifana nophendlo lwesemantic kunye neenkqubo zokucebisa, ezifuna ukutolika iifomathi ezintsonkothileyo zedatha.
Iqonga lakhiwe ngokusebenza kakuhle kunye nokulinganisa engqondweni, elikwaziyo ukuphatha iiseti zedatha ezinkulu kunye neebhiliyoni zamanqaku edatha.
Ibonelela ngeemetrics ezininzi zomgama kubandakanya Ukufana kweCosine, Umgama we-Euclidean, kunye neMveliso yeDot, iyenza ikwazi ukumelana neemeko ezininzi zokusebenzisa.
Uyilo lubonelela ngohluzo oluntsonkothileyo, olunjengomtya, uluhlu, kunye nezihluzi ze-geo, ukuhlangabezana neemfuno ezahlukeneyo zokukhangela.
I-Qdrant ifikeleleka kubaphuhlisi ngeendlela ezahlukeneyo, kubandakanywa nomfanekiso we-Docker wokuseta ngokukhawuleza kwendawo, umxhasi wePython kwabo bakhululekile ngolwimi, kunye nenkonzo yefu yendalo eyomelele ngakumbi, yomgangatho wokuvelisa.
Ukuguquguquka kwe-Qdrant kuvumela ukuhlanganiswa okungenamthungo kunye naluphi na uqwalaselo lweteknoloji okanye iimfuno zenkqubo.
Ngaphaya koko, ujongano olusebenzisekayo lwe-Qdrant lwenza lula ulawulo lwedatha ye-vector. Iqonga lenzelwe ukuba lithe ngqo kubasebenzisi bazo zonke izigaba zezakhono, ukusuka ekudalweni kwamaqela ukuya kwisizukulwana sezitshixo ze-API zokufikelela ngokukhuselekileyo.
Umthamo wayo wokulayisha ngobuninzi kunye ne-asynchronous API iphucula ukusebenza kwayo, iyenza ibe sisixhobo esiluncedo kakhulu kubaphuhlisi abajongana nezixa ezikhulu zedatha.
namaxabiso
Ungaqala ukuyisebenzisa simahla kwaye amaxabiso eprimiyamu aqala ukusuka kwi-25 yeedola kwindawo nganye/ngenyanga ehlawulwa ngeyure.
8. Astra
Amandla okukhangela i-vector ephezulu ye-AstraDB kunye noyilo olungenaseva luguqula usetyenziso lwe-AI oluvelisayo.
I-AstraDB lukhetho olukhulu lokulawula uphendlo oluntsonkothileyo, olunobuntununtunu kwiintlobo ngeentlobo zedatha kuba yakhiwe kwisiseko esiluqilima se-Apache Cassandra kwaye idibanisa ngaphandle komthungo ukuqina, uzinzo kunye nokusebenza.
Amandla e-AstraDB yokusingatha imithwalo yemisebenzi emininzi, kuquka ukusasazwa, i-non-vector, kunye nedatha ye-vector, ngelixa igcina i-latency ephantsi kakhulu yombuzo kunye nokusebenza kohlaziyo, yenye yeenzuzo zayo eziphawulekayo.
Oku kulungelelaniswa kubalulekile kwizicelo ze-AI ezivelisayo, ezifuna ukusasazwa kunye nexesha langempela lokucubungula idatha ukwenzela ukubonelela ngeempendulo ezichanekileyo, ezichanekileyo ze-AI.
Isisombululo esingenamncedisi esivela kwi-AstraDB senza uphuhliso lube lula ngakumbi, ukukhulula abaphuhlisi ukuba bagxininise ekudaleni izicelo ze-AI ezintsha kunokulawula iziseko zokubuyisela umva.
Ukusuka kwisikhokelo esikhawulezayo ukuya kwizifundo ezinzulu ekudaleni ii-chatbots kunye neenkqubo zokucebisa, i-AstraDB yenza abaphuhlisi baqonde ngokukhawuleza iimbono zabo ze-AI ngee-APIs ezithembekileyo kunye nonxibelelwano olugudileyo kunye nezixhobo ezaziwayo kunye namaqonga.
Iinkqubo ze-AI ezivelisa ushishino kufuneka zibeke phambili ukhuseleko kunye nokuthotyelwa, kwaye i-AstraDB inikezela kumacala omabini.
Iimpawu ezinzulu zokhuseleko lwenkampani kunye neziqinisekiso zokuthotyelwa zinikezelwa yiyo, iqinisekisa ukuba izicelo ze-AI eziphuhliswe kwi-AstraDB zithobela ubumfihlo obungqongqo kunye nezikhokelo zokukhusela idatha.
namaxabiso
Ungaqala ukuyisebenzisa simahla kwaye ibonelela ngemodeli yokuhlawula njengoko uhamba.
9. Uphendlo oluVulekileyo
I-OpenSearch ibonakala njengokhetho olunomtsalane kwabo baphonononga ugcino lwedatha ye-vector, ngakumbi ekuphuhliseni iinkqubo eziguquguqukayo, ezinokwehla, kunye nobungqina bexesha elizayo be-AI.
I-OpenSearch yi-database ye-vector equka konke, evulekileyo edibanisa amandla ohlalutyo, ukukhangela i-vector eyinkimbinkimbi, kunye nokukhangela okuqhelekileyo kwinkqubo enye edibeneyo.
Ngokusebenzisa iimodeli zokufakela umatshini wokufunda ukudibanisa intsingiselo kunye nomxholo weefom ezininzi zedatha-amaxwebhu, iifoto, kunye ne-audio-kwii-vectors zokukhangela okufanayo, oku kudityaniswa kunceda ngokukodwa kubaphuhlisi abafuna ukubandakanya ukuqonda kwe-semantic kwii-apps zabo zokukhangela.
Nangona i-OpenSearch inokuninzi enokukunika yona, kubalulekile ukukhumbula ukuba xa kuthelekiswa ne-Elasticsearch, kuye kwakho utshintsho oluninzi lweekhowudi, ngakumbi kwiimodyuli ezibalulekileyo ezifana neelwimi zokubhala kunye nabaqhubekekisi bemibhobho yokungenisa.
I-Elasticsearch inokuba nesakhono esiphucukileyo ngenxa yokwanda kwemizamo yophuhliso, ekhokelela kumahluko ekusebenzeni, iseti yeempawu, kunye nohlaziyo phakathi kokubini.
I-OpenSearch ibuyekeza uluntu olukhulu olulandelayo kunye nokuzinikela kwiingcamango ezivulelekileyo, ezikhokelela kwiqonga elivulekileyo neliguquguqukayo.
Ixhasa uluhlu olubanzi lwezicelo ngaphaya kokukhangela kunye nohlalutyo, olufana nokubonwa kunye nohlalutyo lokhuseleko, okwenza kube sisixhobo esiguquguqukayo semisebenzi enzulu yedatha.
Isicwangciso esiqhutywa luluntu siqinisekisa uphuculo oluqhubekayo kunye nokudibanisa ukugcina iqonga lisexesheni kwaye lahlukile.
namaxabiso
Ungaqala ukuyisebenzisa simahla.
10. Ukukhangela kweAzure AI
Ukukhangela kwe-Azure AI liqonga elomeleleyo eliphucula amandla okukhangela ngaphakathi kwezicelo ze-AI ezivelisayo.
Ibalasele ngenxa yokuba ixhasa uphendlo lwevektha, indlela yokwenza isalathiso, ukugcina, kunye nokuphinda kuthunyelelwe izinto ezizinzisiweyo zevektha ngaphakathi kwesalathiso sokukhangela.
Eli nqaku linceda ekufumaneni amaxwebhu athelekisekayo kwindawo ye-vector, okukhokelela kwiziphumo zophando ezihambelana nomxholo.
Ukukhangela kwe-AI ye-Azure yahlulwe ngenkxaso yayo yeemeko ezixubileyo, apho uphando lwe-vector kunye negama elingundoqo lwenziwa ngaxeshanye, okukhokelela kwisiphumo esidityanisiweyo esihlala sigqwesa ukusebenza kwenkqubo nganye esetyenziswayo yodwa.
Ukudityaniswa kwe-vector kunye ne-non-vector material kwi-index efanayo ivumela amava okukhangela apheleleyo kwaye aguquguqukayo.
Indawo yokukhangela iVector kwi-Azure AI Search ifikeleleka ngokubanzi kwaye isimahla kuzo zonke i-Azure AI Search tiers.
Iguquguquka kakhulu kuluhlu lweemeko zokusetyenziswa kunye nokukhethwa kophuhliso ngenxa yenkxaso yayo kwiindawo ezininzi zophuhliso, ezibonelelwa ngesiza seAzure, REST APIs, kunye nee-SDKs zePython, JavaScript, kunye.NET, phakathi kwabanye.
Ngokudityaniswa nzulu kunye ne-Azure AI ecosystem, i-Azure AI Search ibonelela ngaphezu kokukhangela nje; ikwanyusa amandla e-ikhosistim kwizicelo ze-AI ezivelisayo.
I-Azure OpenAI Studio yokufakela imodeli kunye neeNkonzo ze-Azure ze-AI zokubuyiswa kwemifanekiso yimizekelo emibini kuphela yeenkonzo ezibandakanyiweyo kolu hlanganiso.
Ukukhangela kwe-Azure AI sisisombululo esiguquguqukayo sabaphuhlisi abanqwenela ukubandakanya imisebenzi yokukhangela eyinkimbinkimbi kwizicelo zabo ngenxa yenkxaso yayo ebanzi, eyenza uluhlu olubanzi lwezicelo, ukusuka ekukhangekeni okufanayo kunye nokukhangela kwe-multimodal ukuya kwi-hybrid search kunye nokukhangela kweelwimi ezininzi.
namaxabiso
Unokuqala ukuyisebenzisa simahla kwaye amaxabiso eprimiyamu aqala ukusuka kwi-0.11 yeedola / ngeyure.
isiphelo
Ugcino lwedatha yeVector luguqula ulawulo lwedatha kwi-AI ngokulawula ii-vectors ezinomgangatho ophezulu, ukuvumela ukukhangela okufanayo okuqinileyo kunye nemibuzo ekhawulezayo yommelwane kwizicelo ezifana neenkqubo zokucebisa kunye nokufumanisa ubuqhetseba.
Ngokusetyenziswa kwe-algorithms ye-indexing entsonkothileyo, ezi nkcukacha zolwazi ziguqula idatha entsonkothileyo engacwangciswanga ibe zii-vectors ezinentsingiselo ngelixa ibonelela ngesantya kunye nokuguquguquka okungafunwayo koovimba beenkcukacha bemveli.
Iiplatifti eziphawulekayo ziquka iPinecone, ekhanyayo kwizicelo ze-AI ezivelisayo; I-FAISS, eyenziwe yi-Facebook AI ye-vector clustering exineneyo; kunye ne-Milvus, eyaziwa ngokuba yi-scalability kunye nolwakhiwo lwamafu.
I-Weaviate idibanisa ukufundwa komatshini kunye nophendlo lokuqonda umxholo, ngelixa iVespa kunye neChroma ziphawuleka ngobuchule bazo bekhompyuter obuphantsi-latency kunye nokusebenziseka ngokulula, ngokulandelanayo.
Ugcino lwedatha yeVector zizixhobo ezibalulekileyo zokuphuhlisa i-AI kunye nobuchwepheshe bokufunda koomatshini kuba amaqonga afana ne-Qdrant, i-AstraDB, i-OpenSearch, kunye ne-Azure AI Search zibonelela ngeenkonzo ezahlukeneyo ukusuka kulwakhiwo olungenamncedisi ukuya kukhangelo olubanzi kunye namandla okuhlalutya.
Shiya iMpendulo