Imininingo egciniwe yamaVektha imele ushintsho olukhulu endleleni esiphatha nesihumusha ngayo idatha, ikakhulukazi emikhakheni yobuhlakani bokwenziwa nokufunda kwemishini.
Umsebenzi obalulekile walezi zingosi zolwazi ukuphatha ngempumelelo amavekhtha anobukhulu obuphezulu, okuyimpahla yokusetshenziswa yamamodeli okufunda omshini futhi afaka ukuguqulwa kombhalo, isithombe, noma okokufaka komsindo kube izethulo zezinombolo esikhaleni se-multidimensional.
Kuzinhlelo zokusebenza ezifana namasistimu wokuncoma, ukubonwa kwento, ukutholwa kwesithombe, nokutholwa kokukhwabanisa, lolu shintsho lungaphezu nje kwesitoreji; iwumnyango wamakghono anamandla ekuseshweni okufanayo nemibuzo yomakhelwane abaseduze.
Ngokujule ngokwengeziwe, amandla esizindalwazi se-vector asemandleni azo wokuhumusha inani elikhulu ledatha engahlelekile, eyinkimbinkimbi ibe ama-vector athwebula umongo nencazelo yokuqukethwe kwasekuqaleni.
Imisebenzi yokusesha ethuthukisiwe eyenziwe yenzeke ngokushumeka amamodeli kulokhu kubhala ngekhodi ihlanganisa ikhono lokubuza ama-vector azungezile ukuze kutholwe izithombe noma imisho ehlobene.
Imininingo egciniwe yamaVektha ihlukile ngoba yakhelwe phezu kwamasu okukhomba athuthukile njenge-Inverted File Index (IVF) kanye ne-Hierarchical Navigable Small World (HNSW), ethuthukisa isivinini nokusebenza kahle kwayo kuyilapho ithola omakhelwane abaseduze ezindaweni ezinobukhulu obungu-N.
Kunomehluko ocacile phakathi kwe-vector kanye ne-database yakudala. Imininingo egciniwe evamile mihle kakhulu ekuhleleni idatha ibe amasethi ahlelekile athuthukiswe nge-CRUD futhi abambelela ekusetheni izikimu.
Kodwa-ke, lapho ubhekene nemvelo eguqukayo neyinkimbinkimbi yedatha enobukhulu obuphezulu, lobu kuqina buqala ukuba yisithiyo.
Ngokuphambene, imininingwane egciniwe yamavekhtha inikezela ngezinga lokuguquguquka nokusebenza kahle okulinganayo okungokwesiko okungeke kulingane, ikakhulukazi ezinhlelweni ezithembele kakhulu kuzo. ukufunda imishini kanye nobuhlakani bokwenziwa. Abagcini nje ukukala futhi banolwazi ekusesheni okufanayo.
Imininingo egciniwe yeVector iwusizo ikakhulukazi ezinhlelweni ezikhiqizayo ze-AI. Ukuqinisekisa ukuthi okokusebenza okudaliwe kugcina ubuqotho bomongo, lezi zinhlelo zokusebenza—okuhlanganisa ukucutshungulwa kolimi lwemvelo nokukhiqizwa kwezithombe—kuncike ekutholweni kabusha okusheshayo nokuqhathaniswa kokushumekayo.
Ngakho-ke kulesi siqeshana, sizobheka imininingwane ephezulu ye-vector yephrojekthi yakho elandelayo.
1. Milvus
I-Milvus iyisizindalwazi sevector esivula umthombo ovulekile esiklanyelwe ngokuyinhloko izinhlelo zokusebenza ze-AI, okuhlanganisa ukusesha okushumekiwe kokufana kanye nama-MLOps anamandla.
Ihlukile kusizindalwazi esivamile sobudlelwano, esiphatha kakhulu idatha ehleliwe, ngenxa yaleli khono, eliyenza ikwazi ukukhomba ama-vector esikalini sethriliyoni esingakaze sibe khona.
Ukuzinikela kwe-Milvus ekulinganiseni nasekutholakaleni okuphezulu kuboniswa ngendlela ethuthuke ngayo kusukela enguqulweni yayo yokuqala kuya ku-Milvus 2.0 esatshalaliswe ngokugcwele, yamafu.
Ngokukhethekile, i-Milvus 2.0 ibonisa idizayini egcwele amafu ehlose ukutholakala okumangazayo okungu-99.9% ngenkathi ikala ngaphezu kwamakhulu ama-node.
Kulabo abafuna isixazululo sedatha yedatha ethembekile, lolu hlobo luza ngokunconywa kakhulu ngoba alugcini nje ngokungeza izici eziyinkimbinkimbi njengoxhumano lwamafu amaningi kanye nephaneli yokuphatha, kodwa futhi luthuthukisa amazinga afanayo wedatha ukuze kuthuthukiswe uhlelo lokusebenza oluguquguqukayo.
Inzuzo ephawulekayo ye-Milvus indlela yayo eqhutshwa umphakathi, ehlinzeka ngosekelo lwezilimi eziningi kanye nochungechunge olubanzi lwamathuluzi olwakhelwe izidingo zonjiniyela.
Emkhakheni we-IT, ukulinganisa kwayo kwamafu nokuthembeka, kanye namandla ayo okusesha ama-vector aphezulu kumasethi wedatha amakhulu, kuyenza ibe inketho ethandwayo.
Ukwengeza, ithuthukisa ukusebenza kahle kwemisebenzi yayo isebenzisa amandla okusesha okuxubile ahlanganisa ukusesha kokufana kwe-vector nokuhlunga kwe-scalar.
I-Milvus inephaneli yokuphatha ecacile isikhombimsebenzisi somsebenzisi, isethi egcwele yama-API, kanye nezakhiwo ezingalawuleki futhi ezifundekayo.
Ukuxhumana nezinhlelo zokusebenza zangaphandle kwenziwa lula isendlalelo sokufinyelela, kuyilapho ukulinganisa komthwalo kanye nokuphathwa kwedatha kuhlanganiswa isevisi yomxhumanisi, esebenza njengomyalo omaphakathi.
Ukuhlala unomphela kwesizindalwazi kusekelwa isendlalelo sendawo yokugcina izinto, kuyilapho amanodi ezisebenzi enza imisebenzi yokuqinisekisa ukukala.
Zamanani
Kumahhala ukusetshenziselwa wonke umuntu.
2. FAISS
Ithimba le-Facebook Research AI lithuthukise umtapo wolwazi obizwa nge-Facebook AI Similarity Search oklanyelwe ukwenza ukuhlanganisa ama-vector aminyene kanye nokusesha okufanayo kusebenze kakhudlwana.
Ukudalwa kwayo kwaqhutshwa imfuneko yokuthuthukisa amakhono okusesha afana ne-Facebook AI ngokusebenzisa izindlela eziyisisekelo ezisezingeni eliphezulu.
Uma kuqhathaniswa nokuqaliswa okusekelwe ku-CPU, ukuqaliswa kwe-GPU yesimanjemanje ye-FAAISS kungasheshisa izikhathi zokusesha izikhathi ezinhlanu kuya kweziyishumi, kuyenze ibe ithuluzi eliyigugu lezinhlelo zokusebenza ezihlukahlukene, okuhlanganisa izinhlelo zokuncoma kanye nokuhlonza izincazelo ezifanayo ngosayizi omkhulu. amasethi edatha angahlelekile njengombhalo, umsindo, nevidiyo.
I-FAISS ingakwazi ukuphatha ububanzi obubanzi bemethrikhi yokufana, njengokufana kwe-cosine, umkhiqizo wangaphakathi, kanye ne-metric ye-L2 evame ukusetshenziswa (ibanga le-Euclidean).
Lezi zilinganiso zikwenza kube lula ukwenza usesho olunembile nolula oluvumelanayo kuzo zonke izinhlobo zedatha. Izici ezifana nokucutshungulwa kwenqwaba, ukuhwebelana ngesivinini esinembayo, nosekelo lwakho kokubili ukusesha okunembe nokulinganisa kuqhubeza nokwandisa ukuguquguquka kwayo.
Ukwengeza, i-FAISS inikezela ngendlela engakala yokuphatha amasethi edatha amakhulu ngokuvumela izinkomba ukuthi zigcinwe kudiski.
Ifayela elihlanekezelwe, i-product quantization (PQ), kanye ne-PQ ethuthukisiwe amasu ambalwa nje amasha akha isisekelo socwaningo se-FAISS futhi anezela ekusebenzeni kwayo kahle uma kuziwa ekukhombeni nasekusesheni izinkambu ze-vector ezinobukhulu obuphezulu.
Lawa maqhinga aqiniswa izindlela ezisezingeni eliphezulu ezifana ne-GPU-accelerated k-selection algorithms kanye nokuhlunga kusengaphambili amabanga e-PQ, okuqinisekisa amandla e-FAISS okukhiqiza imiphumela yosesho esheshayo nenembayo ngisho kumadathasethi wesilinganiso esiyizigidi eziyizinkulungwane.
Zamanani
Kumahhala ukusetshenziselwa wonke umuntu.
3. I-Pinecone
I-Pinecone ingumholi kusizindalwazi se-vector, ehlinzeka ngesevisi ephethwe ngamafu, eyakhelwe ngokukhethekile ukuthuthukisa ukusebenza kwezinhlelo zokusebenza ze-AI ezinamandla kakhulu.
Yakhelwe ngokukhethekile ukuphatha ukushumeka kwe-vector, okubalulekile ekukhiqizeni i-AI, ukusesha kwe-semantic, kanye nezinhlelo zokusebenza zisebenzisa amamodeli olimi amakhulu.
I-AI manje ingakwazi ukuqonda ulwazi lwe-semantic ngenxa yalokhu kushumeka, okusebenza ngempumelelo njengenkumbulo yesikhathi eside yemisebenzi eyinkimbinkimbi.
I-Pinecone ihlukile ngoba ihlanganisa kalula amandla esizindalwazi esivamile nokusebenza okuthuthukisiwe kwama-vector indexes, okuvumela ukugcinwa okuphumelelayo nokukhulu kanye nemibuzo yokushumeka.
Lokhu kuyenza ibe inketho efanelekile ezimeni lapho ubunkimbinkimbi nevolumu yedatha ehilelekile kunikeza imininingwane egciniwe esekwe esikalini engenele.
I-Pinecone inikeza abathuthukisi isixazululo esingenazinkinga ngenxa yendlela yayo yesevisi ephethwe, eyenza kube lula ukuhlanganisa kanye nezinqubo zokuthatha idatha ngesikhathi sangempela.
Imisebenzi eminingi yedatha isekelwa yiyo, okuhlanganisa ukulanda, ukubuyekeza, ukususa, ukubuza, kanye nokwenyusa idatha.
I-Pinecone iqinisekisa futhi ukuthi imibuzo emele ukuguqulwa kwesikhathi sangempela okufana nokuphazamiseka nokususwa inikeza izimpendulo ezilungile, ezibambezelekayo eziphansi zezinkomba ezinezigidigidi zamavekhtha.
Ezimweni eziguqukayo, lesi sici sibalulekile ukuze kulondolozwe ukuhambisana nobusha bemiphumela yemibuzo.
Ukwengeza, ukusebenzisana kwe-Pinecone ne-Airbyte ngoxhumo lwe-Pinecone kukhulisa ukuguquguquka kwayo nokuguquguquka, okuvumela ukuhlanganiswa kwedatha okushelelayo kusuka ebangeni lemithombo.
Ngalobu budlelwano, izindleko nokusebenza kahle kungathuthukiswa ngokuqinisekisa ukuthi ulwazi olusanda kutholwa kuphela olusingathwa ngokuvumelanisa kwedatha okukhulayo.
Idizayini yesixhumi igcizelela ubulula, idinga nje ubuncane bemingcele yokusetha, futhi iyanwebeka, ivumela ukuthuthukiswa kwesikhathi esizayo.
Zamanani
Intengo yeprimiyamu iqala ukusuka ku-$5.80/ngenyanga ecaleni lokusebenzisa le-RAG.
4. Shintsha
I-Weaviate isizindalwazi se-vector esisungulayo esitholakala njengesofthiwe yomthombo ovulekile eshintsha indlela esifinyelela ngayo nokusebenzisa idatha.
I-Weaviate isebenzisa amandla okusesha e-vector, anika amandla ukusesha okuyinkimbinkimbi, okuqaphela umongo kuwo wonke amasethi edatha amakhulu, ayinkimbinkimbi, ngokungafani nemininingo egciniwe evamile encike kumanani wesikali kanye nemibuzo echazwe ngaphambilini.
Ngale ndlela, ungathola okuqukethwe ngokusekelwe ekutheni kufana kanjani nokunye okuqukethwe, okuthuthukisa ukuqondisisa kokusesha kanye nokuhambisana kwemiphumela.
Ukuhlanganiswa kwayo okushelelayo namamodeli okufunda komshini kungenye yezici zayo eziyinhloko; lokhu kuvumela ukuthi isebenze njengokungaphezu nje kwesixazululo sokugcina idatha; iphinde ivumele idatha ukuthi iqondwe futhi ihlaziywe kusetshenziswa ubuhlakani bokwenziwa.
Izakhiwo ze-Weaviate zihlanganisa lokhu kuhlanganiswa kahle, okwenza kube nokwenzeka ukuhlaziya idatha eyinkimbinkimbi ngaphandle kokusebenzisa amathuluzi engeziwe.
Ukwesekwa kwayo kwamamodeli edatha yegrafu kuphinde kunikeze umbono ohlukile kudatha njengamabhizinisi axhunyiwe, adalula amaphethini nemininingwane engahle igejwe kuzakhiwo ezivamile zesizindalwazi.
Ngenxa yesakhiwo semojuli ye-Weaviate, amakhasimende angakwazi ukwengeza amakhono afana nokufakwa kwedatha yedatha nokudala ikhophi yasenqolobaneni njengoba kudingeka.
Inguqulo yayo eyisisekelo isebenza njengesizindalwazi sochwepheshe bedatha ye-vector, futhi inganwetshwa namanye amamojula ukuze ihlangabezane nezidingo ezihlukene.
Ukuqina kwayo kuthuthukiswa ngokwengeziwe ukwakheka kwayo kwemojuli, okuqinisekisa ukuthi isivinini ngeke sinikelwe ekuphenduleni amanani edatha akhulayo kanye nezidingo zemibuzo.
Indlela eguquguqukayo nesebenzayo yokusebenzisana nedatha egciniwe yenziwa yenzeke ngokusekelwa kwesizindalwazi sakho kokubili i-RESTful ne-GraphQL API.
Ikakhulukazi, i-GraphQL ikhethwa ngenxa yomthamo wayo wokwenza ngokushesha imibuzo eyinkimbinkimbi, esekelwe kugrafu, evumela abasebenzisi ukuthi bathole idatha abayifunayo ngaphandle kokuthola amanani amaningi noma anganele wedatha.
I-Weaviate isebenziseka kalula kuwo wonke amalabhulali amaklayenti ahlukahlukene nezilimi zokuhlela ngenxa ye-API yayo eguquguqukayo.
Kulabo abafuna ukuhlola i-Weaviate ngokuqhubekayo, kunenqwaba yamadokhumenti nezifundiso ezitholakalayo, kusukela ekusetheni nasekulungiseni isibonelo sakho kuye ekujuleni kumakhono aso afana nosesho lwe-vector, ukuhlanganiswa kokufunda komshini, kanye nedizayini ye-schema.
Ungafinyelela kubuchwepheshe obunamandla obufanayo obenza ulwazi lube namandla futhi lusebenze noma ngabe unquma ukusebenzisa i-Weaviate endaweni, endaweni ngamafu imvelo, noma ngesevisi yefu ephethwe i-Weaviate
Zamanani
Intengo ye-premium yesikhulumi iqala kusuka ku-$25/ngenyanga kwabangenaseva.
5. I-Chroma
I-Chroma isizindalwazi se-vector esisezingeni eliphezulu esihlose ukuguqula ukubuyisa nokugcinwa kwedatha, ikakhulukazi ezinhlelweni ezibandakanya ukufunda komshini nobuhlakani bokwenziwa.
Njengoba i-Chroma isebenza nama-vector esikhundleni sezinombolo ze-scalar, ngokungafani nesizindalwazi esivamile, inhle kakhulu ekulawuleni idatha enobukhulu obuphezulu, eyinkimbinkimbi.
Lokhu kuyintuthuko enkulu kubuchwepheshe bokubuyiswa kwedatha njengoba kunika amandla ukusesha okuyinkimbinkimbi okusekelwe ekufaneni kwe-semantic kokuqukethwe kunokufana kwamagama angukhiye anembayo.
Isici esiphawulekayo seChroma yikhono layo lokusebenza nezixazululo ezimbalwa zokugcina eziyisisekelo, ezifana ne-ClickHouse yezilungiselelo ezilinganiselwe kanye ne-DuckDB yokufakwa okuzimele, okuqinisekisa ukuguquguquka nokuzivumelanisa nezimo ezihlukahlukene zokusetshenziswa.
I-Chroma yenziwe ngobulula, isivinini, nokuhlaziya engqondweni. Itholakala kuhlelo olubanzi lonjiniyela abanama-SDK ePython kanye ne-JavaScript/TypeScript.
Ukwengeza, i-Chroma ibeka ukugcizelela okuqinile kokufaneleka komsebenzisi, okuvumela onjiniyela ukuthi bamise ngokushesha isizindalwazi esihlala njalo esisekelwa i-DuckDB noma isizindalwazi esisememorini ukuze sihlolwe.
Ikhono lokwakha izinto zokuqoqa ezifana namathebula kusizindalwazi esivamile, lapho idatha yombhalo ingafakwa futhi iguqulwe ngokuzenzakalelayo ibe ukushumeka kusetshenziswa amamodeli afana ne-All-MiniLM-L6-v2, ikwandisa nakakhulu lokhu kuhlukahluka.
Umbhalo nokushumeka kungahlanganiswa kalula, okubalulekile ezinhlelweni zokusebenza ezidinga ukubamba idatha ye-semantics.
Isisekelo sendlela yokufana yevekhtha yeChroma imiqondo yezibalo ye-orthogonality kanye nokuminyana, ebalulekile ekuqondeni ukumelwa nokuqhathaniswa kwedatha kusizindalwazi.
Le mibono ivumela i-Chroma ukuthi yenze ukusesha okufanayo okunenjongo nokuphumelela ngokucabangela ukuxhumana kwe-semantic phakathi kwama-elementi edatha.
Izinsiza ezifana nezifundo nemihlahlandlela ziyafinyeleleka kubantu abafuna ukuhlola i-Chroma ngokuqhubekayo. Zihlanganisa isiqondiso sesinyathelo ngesinyathelo sendlela yokusetha isizindalwazi, ukudala amaqoqo, nokusebenzisa ukusesha okufanayo.
Zamanani
Ungaqala ukuyisebenzisa mahhala.
6. Vespa
I-Vespa iyinkundla eguqula ukuphathwa kwe-inthanethi kwe-AI nedatha enkulu.
Injongo eyinhloko ye-Vespa iwukuba uvumele ukubalwa kwezibalo eziphansi kakhulu kuwo wonke amadathasethi amakhulu, okukwenza ukwazi ukugcina kalula, ukukhomba, nokuhlaziya umbhalo, i-vector, nedatha ehlelekile.
I-Vespa ihlukaniswa ngamandla ayo okunikeza izimpendulo ezisheshayo kunoma yisiphi isikali, kungakhathaliseki uhlobo lwemibuzo, ukukhetha, noma ukucatshangwa kwemodeli efundwe ngomshini okusingathwayo.
Ukuvumelana nezimo kweVespa kuboniswa enjinini yayo yokusesha esebenza ngokugcwele kanye nesizindalwazi se-vector, esivumela ukusesha okuningi ngaphakathi kombuzo owodwa, kusukela ku-vector (ANN), idatha ye-lexical, nehlelekile.
Ngaphandle kwesikali, ungakha izinhlelo zokusebenza zosesho ezisebenziseka kalula neziphendulayo ngamakhono esikhathi sangempela se-AI ngenxa yalokhu kuhlanganiswa kwencazelo yemodeli efundwe umshini nedatha yakho.
Nokho, iVespa ingaphezu nje kokufuna; futhi imayelana nokuqonda nokwenza ngokwezifiso ukuhlangana.
Amathuluzi okwenza ngokwezifiso aphezulu kanye neziphakamiso ahlinzeka ngezincomo eziguqukayo, zamanje eziqondiswe kubasebenzisi abathile noma izimo.
I-Vespa ishintsha umdlalo kunoma ubani ofuna ukungena endaweni yezingxoxo ze-AI futhi, njengoba inikeza ingqalasizinda edingekayo ukuze ugcine futhi uhlole idatha yombhalo nevekhtha ngesikhathi sangempela, okuvumela ukuthuthukiswa kwama-agent e-AI athuthuke kakhulu futhi asebenzayo.
Ngokufaka amathokheni abanzi kanye nesisekelo, ukusesha okugcwele umbhalo, ukusesha komakhelwane abaseduze, nemibuzo yedatha ehlelekile konke kusekelwa amandla emibuzo enkundla abanzi.
Iyahluka ngokuthi ingakwazi ukusingatha imibuzo eyinkimbinkimbi ngokuhlanganisa izilinganiso zosesho ezimbalwa.
I-Vespa iwumthombo wamandla wokubala we-AI kanye nezinhlelo zokusebenza zokufunda ngomshini ngoba injini yayo yokubala ingakwazi ukuphatha izinkulumo zezibalo eziyinkimbinkimbi phezu kwama-scalar nama-tensor.
Ngokusebenza, i-Vespa yenziwe ukuthi ibe lula ukuyisebenzisa futhi inganwebeka.
Iqondisa izinqubo eziphindaphindayo, kusukela ekucushweni kwesistimu nokuthuthukiswa kohlelo lokusebenza kuya kudatha kanye nokuphathwa kwamanodi, okuvumela imisebenzi yokukhiqiza evikelekile nengaphazamiseki.
Izakhiwo zeVespa ziqinisekisa ukuthi inweba nedatha yakho, igcina ukwethembeka nokusebenza kwayo.
Zamanani
Ungaqala ukuyisebenzisa mahhala.
7. Quadrant
I-Qdrant iyinkundla yedatha yedatha eguquguqukayo ehlinzeka ngesethi eyingqayizivele yamakhono ukuhlangabezana nezidingo ezikhulayo ze-AI nezinhlelo zokusebenza zokufunda ngomshini.
Esisekelweni sayo, i-Qdrant iyinjini yokusesha efana nevekhtha enikezela nge-API esebenziseka kalula yokugcina, ukuthola, nokugcina ama-vectors kanye nedatha yomthwalo okhokhelwayo.
Lesi sici sibalulekile ezinhlelweni zokusebenza ezimbalwa, ezifana nosesho lwe-semantic nezinhlelo zokuncoma, ezidinga ukuhumusha amafomethi edatha ayinkimbinkimbi.
Inkundla yakhiwe ngokusebenza kahle kanye nokulinganisa engqondweni, ekwazi ukuphatha amasethi edatha amakhulu anezigidigidi zamaphoyinti edatha.
Ihlinzeka ngamamethrikhi amabanga amaningana ahlanganisa i-Cosine Similarity, i-Euclidean Distance, kanye ne-Dot Product, iyenze ikwazi ukuzivumelanisa nezimo kuzo zonke izimo zokusetshenziswa.
Idizayini inikeza ukuhlunga okuyinkimbinkimbi, okufana neyunithi yezinhlamvu, ububanzi, nezihlungi ze-geo, ukuhlangabezana nezidingo zosesho ezihlukahlukene.
I-Qdrant ifinyeleleka konjiniyela ngezindlela ezihlukahlukene, okuhlanganisa isithombe se-Docker sokusetha kwendawo ngokushesha, iklayenti le-Python lalabo abakhululekile ngolimi, kanye nesevisi yefu yendawo eqinile, yezinga lokukhiqiza.
Ukuvumelana nezimo kwe-Qdrant kuvumela ukuhlanganiswa okungenamthungo nanoma yikuphi ukucushwa kobuchwepheshe noma izidingo zenqubo.
Ngaphezu kwalokho, isixhumi esibonakalayo esisebenziseka kalula se-Qdrant senza ukuphathwa kwesizindalwazi se-vector kube lula. Inkundla yenzelwe ukuthi iqonde kubasebenzisi bawo wonke amaleveli wamakhono, kusukela ekudalweni kwamaqembu kuya ekukhiqizeni okhiye be-API ukuze bafinyeleleke ngokuphephile.
Amandla ayo okulayisha ngobuningi kanye ne-API engavumelanisi ithuthukisa ukusebenza kahle kwayo, okuyenza ibe ithuluzi eliwusizo kakhulu konjiniyela ababhekana nenani elikhulu ledatha.
Zamanani
Ungaqala ukuyisebenzisa mahhala futhi amanani entengo aqala kusuka ku-$25 inodi ngayinye/ngenyanga akhokhiswa njalo ngehora.
8. I-Astra DB
Amandla okusesha ama-vector aphezulu e-AstraDB kanye nezakhiwo ezingenasiphakeli ziguqula izinhlelo zokusebenza ze-AI ezikhiqizayo.
I-AstraDB iyinketho enhle yokuphatha ukusesha okuyinkimbinkimbi, okuzwela umongo kuzo zonke izinhlobo zedatha njengoba yakhelwe phezu kwesisekelo esiqinile se-Apache Cassandra futhi ihlanganisa ngaphandle komthungo ukuqina, ukuqina, nokusebenza.
Amandla e-AstraDB okusingatha imithwalo yemisebenzi ehlukahlukene, okuhlanganisa ukusakaza, okungeyona i-vector, nedatha ye-vector, kuyilapho ilondoloza ukubambezeleka okuphansi kakhulu kokubuza kanyekanye nokusebenza kokuvuselela, kungenye yezinzuzo zayo eziphawuleka kakhulu.
Lokhu kuzivumelanisa nezimo kubalulekile ezinhlelweni ezikhiqizayo ze-AI, ezidinga ukusakaza-bukhoma nokucutshungulwa kwedatha ngesikhathi sangempela ukuze kuhlinzekwe izimpendulo ezinembile, eziqaphela umongo we-AI.
Isixazululo esingenasiphakeli esivela ku-AstraDB senza ukuthuthukiswa kube lula nakakhulu, sikhulula abathuthukisi ukuthi bagxile ekudaleni izinhlelo zokusebenza ze-AI ezisha kunokuphatha ingqalasizinda ye-backend.
Kusukela ekuqondisweni kokuqala okusheshayo kuya ezifundweni ezijulile zokudala ama-chatbots nezinhlelo zokuncoma, i-AstraDB inika amandla abathuthukisi ukuthi babone ngokushesha imibono yabo ye-AI ngama-API athembekile kanye nokuxhumana okushelelayo ngamathuluzi nezinkundla ezaziwayo.
Izinhlelo ze-AI ezikhiqizayo ze-Enterprise-grade kufanele zibeke phambili ukuphepha nokuhambisana, futhi i-AstraDB iletha kuzo zombili izinhlangothi.
Izici ezijulile zokuphepha zenkampani nezitifiketi zokuthobela zihlinzekwa yiyo, iqinisekisa ukuthi izinhlelo zokusebenza ze-AI ezakhiwe ku-AstraDB zithobela ubumfihlo kanye nemihlahlandlela yokuvikela idatha.
Zamanani
Ungaqala ukuyisebenzisa mahhala futhi inikeza imodeli yokukhokha njengoba uhamba.
9. I-OpenSearch
I-OpenSearch ibonakala njengenketho ekhangayo yalabo abahlola isizindalwazi se-vector, ikakhulukazi ekuthuthukiseni amasistimu e-AI aguquguqukayo, angalawuleki, futhi anobufakazi besikhathi esizayo.
I-OpenSearch ihlanganisa konke, isizindalwazi se-vector esivula umthombo ovulekile esihlanganisa amandla ezibalo, ukusesha kwe-vector okuyinkimbinkimbi, nosesho oluvamile lube yisistimu eyodwa ehlangene.
Ngokusebenzisa amamodeli wokushumeka okufunda ngomshini ukuze ufake ikhodi incazelo nomongo wamafomu amaningi edatha—amadokhumenti, izithombe, nomsindo—kumavekhtha okusesha okufanayo, lokhu kuhlanganiswa kusiza kakhulu onjiniyela abafuna ukufaka ukuqonda kwe-semantic ezinhlelweni zabo zokusebenza zosesho.
Yize i-OpenSearch inokuningi okunikezwayo, kubalulekile ukukhumbula ukuthi uma kuqhathaniswa ne-Elasticsearch, kube khona izinguquko ezimbalwa kakhulu zamakhodi, ikakhulukazi kumamojula abalulekile njengezilimi zokubhala kanye namaphrosesa wepayipi lokungenisa.
I-Elasticsearch ingaba namakhono ayinkimbinkimbi ngenxa yokwanda komzamo wokuthuthukisa, okuholela kumehluko ekusebenzeni, ukusetha isici, nezibuyekezo phakathi kwakho kokubili.
I-OpenSearch inxephezela ngomphakathi omkhulu olandelayo kanye nokuzinikela emibonweni yomthombo ovulekile, okuholela kuplathifomu evulekile nevumelana nezimo.
Isekela izinhlelo zokusebenza eziningi ngaphandle kokusesha nokuhlaziya, njengokubonwa nokuhlaziya ukuphepha, okuyenza ibe ithuluzi eliguquguqukayo lemisebenzi edinga idatha.
Isu eliqhutshwa umphakathi liqinisekisa izithuthukisi eziqhubekayo nokuhlanganiswa ukuze kugcinwe inkundla isesikhathini futhi ihlukile.
Zamanani
Ungaqala ukuyisebenzisa mahhala.
10. Usesho lwe-Azure AI
I-Azure AI Search iyinkundla eqinile ethuthukisa amakhono okusesha ngaphakathi kwezinhlelo zokusebenza ezikhiqizayo ze-AI.
Igqamile ngoba isekela ukusesha kwe-vector, indlela yokukhomba, ukugcina, kanye nokubuyisa okushumekiwe kwe-vector ngaphakathi kwenkomba yokusesha.
Lesi sici sisiza ukuthola amadokhumenti aqhathanisekayo esikhaleni se-vector, okuholela emiphumeleni yosesho ehlobene kakhulu nomxholo.
I-Azure AI Search ihlukaniswa ngokusekela kwayo izimo ezixubile, lapho ukusesha kwe-vector kanye namagama angukhiye kwenziwa kanye kanye, okuholela esimisweni somphumela obumbene esivame ukwedlula ukusebenza kahle kwendlela ngayinye esetshenziswa iyodwa.
Inhlanganisela ye-vector ne-non-vector material enkombeni efanayo ivumela ulwazi oluphelele noluguquguqukayo lokusesha.
Isici sokusesha i-vector ku-Azure AI Search sifinyeleleka kabanzi futhi mahhala kuwo wonke ama-Azure AI Search tiers.
Iguquguquka ngokwedlulele kuhlu lwamacala okusetshenziswa nokuthandwayo kwentuthuko ngenxa yokusekelwa kwayo kwezindawo ezimbalwa zokuthuthuka, ezihlinzekwa ngesiza se-Azure, REST APIs, kanye nama-SDK ePython, JavaScript, kanye.NET, phakathi kokunye.
Ngokuhlanganiswa kwayo okujulile ne-Azure AI ecosystem, i-Azure AI Search inikeza okungaphezu kokusesha nje; iphinde ithuthukise amandla e-ecosystem wezinhlelo zokusebenza ezikhiqizayo ze-AI.
I-Azure OpenAI Studio yokushumeka imodeli kanye Nezinsizakalo ze-Azure AI zokuthola izithombe ziyizibonelo ezimbili kuphela zezinsizakalo ezifakwe kulokhu kuhlanganiswa.
I-Azure AI Search iyisixazululo esivumelana nezimo sabathuthukisi abafisa ukufaka imisebenzi yokusesha eyinkimbinkimbi ezinhlelweni zabo zokusebenza ngenxa yokusekelwa kwayo okubanzi, okwenza kube nezinhlelo eziningi zokusebenza, kusukela ekusesheni okufanayo nasekuseshweni kwezindlela eziningi kuya ekusesheni okuxubile nokusesha ngezilimi eziningi.
Zamanani
Ungaqala ukuyisebenzisa mahhala futhi amanani entengo aqala ku-$0.11/ihora.
Isiphetho
Imininingo egciniwe ye-Vector iguqula ukuphathwa kwedatha ku-AI ngokuphatha ama-vector anobukhulu obuphezulu, okuvumela ukusesha okuqinile okufanayo nemibuzo esheshayo yomakhelwane ezinhlelweni zokusebenza ezifana nezinhlelo zokuncoma nokutholwa kokukhwabanisa.
Ngokusetshenziswa kwama-algorithms ezinkomba ayinkimbinkimbi, lezi zingosi zolwazi ziguqula idatha eyinkimbinkimbi engahlelekile ibe amavekhtha anengqondo kuyilapho ihlinzeka ngesivinini kanye nokuguquguquka okungenziwa yisizindalwazi esivamile.
Izinkundla eziphawulekayo zihlanganisa i-Pinecone, ekhanya ezinhlelweni ezikhiqizayo ze-AI; I-FAISS, eyakhiwe yi-Facebook AI yokuhlanganisa ama-vector aminyene; kanye ne-Milvus, edume ngokuqina kwayo kanye nezakhiwo ezitholakala efwini.
I-Weaviate ihlanganisa ukufunda komshini nosesho oluqaphela umongo, kuyilapho i-Vespa ne-Chroma ziphawuleka ngamakhono azo ekhompuyutha abambezeleke kancane kanye nokusebenziseka kalula, ngokulandelana.
Imininingo egciniwe yeVector ingamathuluzi abalulekile okuthuthukisa i-AI kanye nobuchwepheshe bokufunda komshini njengoba amapulatifomu afana ne-Qdrant, i-AstraDB, i-OpenSearch, ne-Azure AI Search ihlinzeka ngezinsizakalo ezahlukahlukene ukusuka ekwakhiweni kwe-server okungenamaseva kuya kumakhono okusesha nokuhlaziya.
shiya impendulo