Izizindalwazi zeVektha Ezilula Zemithwalo Yemisebenzi Yesimanje ye-AI

Ikhaya » Python » Izizindalwazi zeVektha Ezilula Zemithwalo Yemisebenzi Yesimanje ye-AI

Lesi sihloko sihamba ngendlela yesizindalwazi se-vector ngokugxila okukhethekile ezinkethweni ezilula, ezikwi-prem: ukuthi i-vector DB iyini ngempela, ukuthi ihluke kanjani ku-vector index ecacile, ukuthi inika amandla kanjani i-NLP ne-RAG, yiziphi izinjini nezandiso ezifanele ukucatshangelwa (kusukela ku-Milvus no-Qdrant kuya ku-PostgreSQL pgvector kanye nemitapo yolwazi efakiwe njenge-VDB), nokuthi ama-distance metrics kanye nama-algorithms e-ANN athinta kanjani ikhwalithi kanye nokusebenza.

Iyini i-vector database futhi kungani ibalulekile?

Izizindalwazi zobudlelwano zendabuko zikhanya kudatha ehlelekile emigqeni nasemakholomu, kodwa bayahlupheka uma ubaphonsa inqwaba yokuqukethwe okungahlelekile. Ukulayisha ama-PDF, izingodo zengxoxo, izithombe noma idatha yenzwa ku-schema ye-SQL yakudala bese ubalungiselela i-AI akugcini nje ngokudina, kodwa futhi akuphumelelanga ngokwezibalo uma udinga ukufana kwencazelo esikhundleni sokufana okuqondile.

Izizindalwazi ze-Vector zixazulula lokhu ngokusebenza ngqo nama-vector aminyene esikhundleni samathokheni noma amagama angukhiye kuphelaEsikhundleni sokubuza ukuthi “ingabe le nsimu iqukethe igama elithi smartphone?”, ubuza ukuthi “yimaphi amavektha agciniwe aseduze kakhulu nokushumeka kombuzo?”, bese uhlelo lubuyisela izinto ezihlobene nencazelo ngisho noma zingabelani ngamagama afanayo.

Lokhu kushintsha kusuka ekufanisweni kwamagama angukhiye kuya ekufaneni esikhaleni sevektha yikona okuvumela usesho lwe-semantic, izincomo eziqinile kanye nokukhiqizwa okunamandla okuthola kabusha (i-RAG)Izinkampani manje sezingakwazi ukuhlanganisa idatha yazo yebhizinisi yendabuko "nenkumbulo ye-semantic" kusakhiwo esisodwa, kungaba ngezinjini ze-vector ezizinikele noma ngokunika amandla izinhlobo ze-vector ngaphakathi kwesizindalwazi esikhona.

Ama-vector, ukushumeka kanye nenkinga abayixazululayo ngempela

Enhliziyweni yanoma iyiphi i-database ye-vector kukhona ama-vector: uhlu oluhlelekile lwezinombolo oluthola into esikhaleni esinobukhulu obuningi. Ivektha ngayinye ihambisana nento – umusho, isigaba, isithombe, umkhiqizo, iphrofayela yomsebenzisi – ebhalwe ngezinhlamvu eziningi, amakhulu noma ngisho nezinkulungwane zobukhulu obufundwe yimodeli yokufunda komshini.

Amamodeli ahlukene okushumeka achaza izikhala zevektha ezahlukene kanye nobukhuluAmanye angase akhiphe amavekhtha angu-384, amanye angu-768 noma ngaphezulu; njengoba ubukhulu bukhula, ukumelwa kungabamba izici ezicebile kodwa futhi kuba nzima kakhulu ukukhomba kahle. Izizindalwazi zamavekhtha zigxile ekuphatheni lokhu ngqo: amavekhtha amade antantayo esikalini.

Ubuhlungu bangempela ababuxazulula ukuqina kokusesha amagama angukhiye endabuko kudatha engahlelekileUkusesha okuvamile kwe-"smartphone" kuzolahlekelwa amadokhumenti akhuluma kuphela ngokuthi "iselula" noma "idivayisi yeselula"; ukusesha amagama angukhiye abekezelela ukubhalwa kwephutha kuyasiza kancane, kodwa akukaqondi ngempela ukuthi "indlu yesimanje yaphakathi nekhulu leminyaka enokukhanya kwemvelo" iyisitayela, hhayi umusho oqondile ozowuthola kuzo zonke izinhla.

Ngokugcina okufakiwe, isizindalwazi sevektha sivumela ukusesha okufana: imibuzo namadokhumenti kokubili kuyivektha, futhi ukusondelana kuleso sikhala kumelela ukuhlobana kwencazeloYingakho ukusesha "iselula" kungabuyisa amadokhumenti akhuluma kuphela "nge-smartphone"; ukushumeka kwawo kuhlala endaweni efanayo yesikhala, ngisho noma kunezinhlobo ezahlukene zobuso.

Inkomba yeVektha vs isizindalwazi esigcwele sevektha

Kuwusizo ukuhlukanisa umqondo "wenkomba yevektha" kulowo wedathabheyisi yevektha egcweleZombili zibhekana nama-vector, kodwa zibhekana nezendlalelo ezahlukene zenkinga futhi ziza namasethi ezici ahlukene.

Inkomba yevektha iyisakhiwo sedatha esenzelwe ukusesha omakhelwane abaseduzeUyinika isethi yamavektha kanye nevektha yombuzo, futhi ikutshela ukuthi yiziphi izinto ezigciniwe eziseduze kakhulu. Amalabhulali afana ne-FAISS amahle kakhulu kulokhu; asebenzisa ama-algorithms asebenzayo okusesha kanye nokuqoqana komakhelwane abaseduze (i-ANN), kodwa akuzona izinhlelo ezigcwele zedathabheyisi.

I-database ye-vector, ngokuphambene nalokho, isonga lawo ma-index ngamakhono e-database njengokugcinwa kwemethadatha, ukuphathwa kweskimu, ukuphepha, ukuphathwa kwezinsizakusebenza, ukulawulwa kwemali ehambisanayo, ukubuyiselwa kokuhluleka, kanye nokuhlanganiswa nezinhlelo zemvelo zedatha ezibanzi. Yilapho izinhlangano zigcina khona kokubili ukushumeka kanye nezinto zokuqala (noma izinkomba kuzo), hhayi nje izakhiwo zenkomba.

Izizindalwazi zevekhtha ezilungele ibhizinisi nazo zidalula izilimi zemibuzo nama-API ahlanganisa ukufana kwevekhtha nezihlungi ezicini ezihlelekile. Ungase ubuze "amadokhumenti afana nalesi sigaba, lapho iphrojekthi = X kanye ne-created_at zingaphakathi kwezinsuku ezingu-30 ezedlule", into okunzima ukuyenza kahle ngelayibrari yezinkomba kuphela.

Ezinye izinhlelo zokuxhumana zanamuhla sezibe “yidathabheyisi enikwe amandla yivektha” ngokungeza izinhlobo zevektha zomdabuIsibonelo, i-Oracle Database kanye ne-MySQL manje zisekela ama-vector kanye nezinkambu zezinombolo zakudala kanye nombhalo. Lokho kukuvumela ukuthi ugcine amarekhodi ebhizinisi kanye nokushumeka enjinini eyodwa, ugweme ukungezwani phakathi kwesitolo se-vector esihlukile kanye nesizindalwazi sakho esiyinhloko.

Indlela ama-database e-vector anika ngayo amandla i-NLP kanye ne-AI yokukhiqiza

Ukusesha nge-Semantic kungenye yezindlela zokusetshenziswa ezibonakala kakhuluEsikhundleni sokufanisa amagama angukhiye angabonakali, ufaka umbuzo womsebenzisi kanye nawo wonke amadokhumenti anenkomba, bese uthola lawo anama-vector aseduze kakhulu. Uhlelo lungaphatha amagama afanayo, amagama aphindaphindwayo kanye nokubekwa kwamagama aphambene kancane nesihloko kodwa ahlobene nomongo, okuthuthukisa kakhulu ukufaneleka kokusesha umbhalo ocacile.

Lolu ngqimba lwencazelo luphinde lunciphise umthelela wokubhala amagama kanye nolimi olunomsindoUmsebenzisi akudingeki abeke umbuzo ngendlela efanele; uma nje incazelo iyonke ifana, imodeli yokushumeka ibeka umbuzo eduze kwamadokhumenti afanele futhi i-vector DB iyawaveza.

Ukuphathwa kokushumeka okuphumelelayo kungenye indima ebalulekileAma-database e-vector alungiselelwe ukugcina, ukukhomba nokubuyisa amavolumu amakhulu okushumeka kombhalo okukhiqizwe amamodeli amakhulu; avumela izinhlelo zokusebenza ukuthi ziphathe lokhu njenge-"memory bank" esheshayo, engabuzwa engafinyelelwa ngama-millisecond, kunokuba kube yiqoqo lamafayela noma ama-ad-hoc array enkambisweni ethile yesicelo. ukushumeka okukhiqizwe amamodeli amakhulu ngokuvamile zithembele kuzikhathi zokusebenza kanye nama-accelerator ukuze zisebenze kahle ngezinga.

Empeleni, lokhu kubonakala ezinhlelweni eziningi ze-NLP: ama-chatbot kanye nabasizi be-AI basebenzisa ama-vector DB ukubheka izingxenye ezifanele zezingxoxo noma imibhalo yangaphambilini; Izinhlelo ze-Q&A ziguqula imibhalo ibe ukushumeka futhi ziphendule imibuzo eyinkimbinkimbi ngokuthola nokuhlanganisa izindima ezifanele; ukuhlaziywa kwemizwa kanye nenhloso kuzuza ebuhlotsheni obucebile be-semantic obufakwe kuma-vector; izinjini zokuncoma ziphetha ngokufana phakathi kwezinto nabasebenzisi ngokusekelwe ekusondelaneni kwazo kwesikhala sokushumeka.

Ukusesha amavektha ekubuyiseleni okungeziwe (i-RAG)

Isizukulwane esingeziwe sokubuyisa (i-RAG) sihlanganisa ukusesha kwe-vector namamodeli amakhulu olimi ukuze kuncishiswe izinkinga ezifana nokubona izinto ezingekho kanye nolwazi oludalaAma-LLM anomkhawulo wokuqeqeshwa ohleliwe futhi awakwazi ukubona amadokhumenti akho obunikazi ngaphandle kokuthi uwanikeze ngokucacile ngesikhathi sokuphetha.

Ipayipi elijwayelekile le-RAG liqala ngokuhlukanisa isisekelo sakho solwazi sibe izingxenye ezincane – isibonelo amagama angu-200-500 ngesiqeshana ngasinye sombhalo – bese ubhala ikhodi yesiqeshana ngasinye kusiqeshana sokushumeka kusetshenziswa imodeli ekhethiwe. Lawa mavektha, kanye nemethadatha efana nezihloko, amathegi noma ama-URL omthombo, agcinwa kusiqeshana sedatha sesiqeshana.

Uma umsebenzisi ebuza umbuzo, uhlelo lufaka umbuzo ngemodeli efanayo futhi yenza usesho olufanayo ngokumelene nokushumeka okugciniwe. Izingcezu eziseduze kakhulu ze-top‑k zicatshangwa ukuthi “zimayelana” nombuzo futhi zitholakala ngama-millisecond, ngenxa yezinkomba ze-ANN ze-DB.

Izingcezu ezitholiwe bese zilungiselelwa noma zifakwe ngenye indlela ku-LLM promptLena yingxenye "yokwandisa": imodeli ithola kokubili isicelo somsebenzisi sokuqala kanye nezingcezu eziningana ezifanele zomongo wangaphandle, okusiza ukuthi isekele impendulo yayo emaqinisweni kunokuba iqagele.

Ekugcineni, i-LLM ikhiqiza impendulo ehambisana nalo mongo otholiweNgenxa yokuthi okuqukethwe kwedatha kungabuyekezwa njalo, i-RAG ivumela ama-LLM ukuthi aphendule esebenzisa ulwazi olusesikhathini, oluqondene nesizinda ngaphandle kokuqeqesha kabusha imodeli ngokwayo, futhi inciphisa ukubona izinto ezingekho ngokuqinisa imiphumela kumadokhumenti angempela.

Indlela ukusesha okufana okusebenza ngayo ngempela

Ngaphansi kwe-hood, ukusesha kwevektha kumayelana nokuqhathanisa i-vektha yombuzo namavektha amaningi agciniwe bese uwabeka ezingeni ngebanga noma amaphuzu okufanaInselele ukwenza lokhu ngokushesha nangokunembile uma unezigidi noma izigidigidi zama-vector ngobukhulu obuphezulu.

Izinyathelo eziyisisekelo ziyahambisana kuzo zonke izinjiniOkokuqala, uhlela idatha yakho nge-vektha: umbhalo, izithombe, umsindo noma okunye okuqukethwe kondliwa ngemodeli yokushumeka ukuze kukhiqizwe ama-vektha. Okulandelayo, ugcina lawo ma-vektha kusizindalwazi, ngokuvamile kanye nama-ID kanye ne-metadata, bese wakha izinkomba ze-ANN eyodwa noma ngaphezulu phezulu.

Ngesikhathi sombuzo, okokufaka komsebenzisi nakho kufakwe ku-vectorIsizindalwazi bese sisebenzisa inkomba ukuthola omakhelwane abaseduze ngokuphathelene ne-metric ekhethiwe - ukufana kwe-cosine, ibanga le-Euclidean, umkhiqizo wangaphakathi noma okunye - bese kubuyisela ukufana okuphezulu kanye namaphuzu abo okufana.

Imiphumela ivame ukuhlukaniswa ngesilinganiso sokufana ukuze amavektha aseduze avele kuqalaIzinjini eziningi zisekela nemibuzo exubile, lapho uhlunga khona ngemethadatha (isibonelo ububanzi bamanani, indawo, isigaba) ngenkathi ngesikhathi esifanayo ulungiselela ukufana kwevektha, okukunikeza imiphumela eminingi eqaphelwa yibhizinisi.

Ukuze konke lokhu kusheshe ngezinga, izizindalwazi ze-vector zanamuhla zithembele kuma-algorithms aseduze aseduzeBashintshanisa ukukhunjulwa okuncane ukuze bathole ukuthuthukiswa okukhulu kwesivinini kanye nokusetshenziswa kwememori, okuyinto eyamukelekayo ezinhlelweni eziningi ze-AI zomhlaba wangempela.

Ama-algorithms ayisihluthulelo e-ANN: i-HNSW, i-LSH kanye ne-Product Quantization

I-Hierarchical Navigable Small World (HNSW) ingenye yama-algorithms e-ANN asetshenziswa kakhulu kuma-database e-vectorIhlela ama-vector abe yizingqimba eziningi zegrafu: izingqimba ezingaphezulu zinama-node ambalwa kanye nokuxhumana okude, kuyilapho izingqimba ezingezansi ziba nzima kakhulu, wonke ama-node axhunywe kungqimba olungezansi.

Ngesikhathi sokusesha, i-HNSW iqala endaweni yokungena engqimbeni ephezulu bese ihamba ngobugovu ibheke komakhelwane abaseduze., ukuhambisa phansi izendlalelo njengoba kuthuthukisa usesho. Lesi sakhiwo segrafu esinezendlalelo sinikeza ibhalansi ephumelelayo phakathi kokukhumbula kanye nokubambezeleka, yingakho i-HNSW inika amandla izinjini ezifana ne-Milvus, i-Qdrant nezinye.

I-Locality-Sensitive Hashing (LSH) isebenzisa indlela ehlukile, isebenzisa imisebenzi ye-hash ehlanganisa ama-vector afanayo emabhakedeni afanayo ngamathuba aphezuluNgokungafani ne-hashing yendabuko ezama ukugwema ukungqubuzana, i-LSH iyawamukela ezintweni ezifanayo. Amatafula amaningi e-hash akhiwe ukuze umbuzo ngamunye udinga kuphela ukuhlola abantu abazongenela ukhetho kusukela kumabhakede afanayo esikhundleni sedatha ephelele.

Lokhu kunciphisa ngempumelelo ubukhulu ngenkathi kulondolozwa isakhiwo sendawo ngendlela engenzekaI-LSH ingaba yinhle kakhulu ngedatha esezingeni eliphezulu uma udinga ukukhiqizwa okusheshayo kakhulu kwezibalo futhi ungabekezelela imiphumela elinganiselwe.

Ukulinganiswa Komkhiqizo (i-PQ) kugxila ekucindezeleni ama-vector ukuze kulondolozwe inkumbulo futhi kusheshiswe ukubalwa kwebangaIhlukanisa i-vector ngayinye enobukhulu obuphezulu ibe ama-subvector amaningana, bese ilinganisa isikhala ngasinye esingaphansi ngokwahlukana bese igcina ama-ID kuphela ama-centroid aseduze, yakha ikhodi emfushane.

Lokhu kucindezelwa kunganciphisa ukusetshenziswa kwememori ngaphezu kuka-90% ngenkathi kusavumela ukulinganisa ibangaNakuba i-PQ ilahlekile futhi inganciphisa ukunemba kokusesha kancane, inamandla kakhulu kumaqoqo amakhulu lapho i-RAM iyisihibe esiyinhloko, futhi iyisisekelo samathuluzi afana ne-FAISS kanye namanye ama-backend e-vector DB.

Izilinganiso zebanga: I-Euclidean vs i-cosine nabangani

Ikhwalithi yokusesha kwakho kwe-vector incike kakhulu ebangeni noma kumethrikhi yokufana oyikhethayoOkubili kwezinketho ezivame kakhulu yibanga le-Euclidean (L2) kanye nokufana kwe-cosine (noma ukuhambisana kwayo, ibanga le-cosine).

Ibanga le-Euclidean lilinganisa ibanga lomugqa oqondile phakathi kwamaphuzu amabili esikhaleni esingu-n-dimensionalKumavekhtha u-P no-Q, kuyimpande yesikwele yesamba somehluko wama-coordinate ayisikwele. Ibanga elifushane lisho ukufana okukhulu, futhi ububanzi balo busuka ku-0 (amavekhtha afanayo) buye ku-infinity.

Lesi silinganiso sibucayi ngobukhuluUma i-vector eyodwa inde kakhulu kunenye – isibonelo, imelela idokhumenti ende noma amanani esici amakhulu – ibanga le-Euclidean lizobonisa lokho, noma ngabe womabili ama-vector akhomba cishe ohlangothini olufanayo. Kusebenza kahle lapho isikali esiphelele sinencazelo yesimantiki, isib. ama-coordinates angokwenyama noma izici zezinombolo eziqhubekayo lapho usayizi ubalulekile khona.

Ukufana kwe-cosine, ngokuphambene nalokho, kubheka i-engeli ephakathi kwama-vector amabili, hhayi ubude bawo. Ungumkhiqizo wamachashazi ohlukaniswe ngomkhiqizo wezimiso ze-vector. Izinhlelo eziningi ezisebenzayo zisebenzisa ibanga le-cosine = 1 − ukufana kwe-cosine, lapho u-0 esho isiqondiso esifanayo kanti amanani amakhulu asho ukufana okwengeziwe.

Ngenxa yokuthi ayinaki ubukhulu, ukufana kwe-cosine kuhle kakhulu lapho ukuqondiswa kuhlanganisa ama-semanticsEkusetshenzisweni kombhalo, imibhalo emibili ngesihloko esifanayo - eyodwa emfushane nenye ende - kufanele isabhekwa njengefana kakhulu; i-cosine yenza lokho kwenzeke, kanti ibanga le-Euclidean lingase lijezise idokhumenti ende ngokubala okukhulu.

Ezindaweni eziphakeme nezincane ezivamile kwi-NLP, ukufana kwe-cosine kuvame ukusebenza ngokuqinile kunebanga le-Euclidean"Isiqalekiso sobukhulu" senza wonke amabanga e-Euclidean aqale ukubukeka afanayo ngobukhulu obuphezulu kakhulu, okunganciphisa amandla okuhlukanisa. I-Cosine isebenza kuma-vector ajwayelekile futhi ivame ukuveza ukuhleleka okufana okunenjongo kokushumeka kombhalo.

Ukukhetha i-metric kuncike ekutheni ufuna ukuthini "ukufana" esizindeni sakhoUma isikali sibalulekile – isibonelo, ukutholwa okungafani ngokusekelwe ebukhulu bokuphambuka – i-Euclidean ingafaneleka. Uma ukusondelana kwesihloko noma ukuqondanisa okuqondisiwe kubaluleke kakhulu kunobude, i-cosine ngokuvamile iyona efaneleka kangcono. Amanye ama-database aveza umkhiqizo wangaphakathi njenge-metric, ehlobene eduze ne-cosine lapho ama-vector elungiswa.

Izizindalwazi ze-vector ezidumile kanye nezinhlelo ezinikwe amandla yi-vector

I-ecosystem yezinketho zokugcina ama-vector iqhume kakhulu, kusukela kumasevisi efu aphethwe ngokugcwele kuya kuzinjini zomthombo ovulekile ezizibamba zona kanye nezixazululo zesitayela selabhulali.Ukukhetha okulungile kuncike esikalini sakho, isabelomali, imikhawulo yokusebenza kanye nokuthi ufuna ukuhlanganisa kahle kangakanani nengqalasizinda yedatha ekhona.

Ama-database e-vector anikezelwe akhiwe kusukela phansi ukuze kuseshwe ukufana okuphezuluNgokuvamile zisekela ama-index amaningi e-ANN, izinhlelo zokucindezela eziyinkimbinkimbi, ukuhlunga i-metadata ecebile kanye nokuhlanganiswa kwebanga lokukhiqiza kanye nokuhluleka.

I-Milvus iyisibonelo esihle sedathabheyisi enamandla ye-vector yomthombo ovulekile eyenzelwe imithwalo yemisebenzi emikhuluIhlose ukufunda komshini, ukufunda okujulile, izinhlelo zokusesha okufana kanye nezincomo, futhi isekela ukusheshisa kwe-GPU, imibuzo esabalele kanye nezindlela ezahlukahlukene zokufaka izinkomba njenge-IVF, i-HNSW kanye ne-PQ.

Lokhu kulungiselelwa kukuvumela ukuthi ulinganisele ukukhunjulwa, ukubambezeleka kanye nokugcinwa kwesitoreji ngokwezidingo zakhoI-Milvus ifaneleka kahle amabhizinisi anezigidigidi zama-vector, okuqukethwe kwezilimi eziningi kanye nezidingo zokusebenza okuqinile, futhi ihlanganiswa kahle kumapulatifomu edatha ayinkimbinkimbi.

Ezinye izinjini ezizinikele zigcwalisa ama-niches ahlukene kancane. I-Pinecone igxile ekusetshenzisweni kwamafu okulawulwa ngokugcwele ngama-SLA aqinile kanye namakhono e-metadata aqinile; I-Weaviate inikeza injini yomthombo ovulekile ene-GraphQL API, ama-vectorisers akhelwe ngaphakathi kanye ne-hybrid keyword + ukusesha kwe-vector; I-Qdrant inikeza isevisi yokusesha i-vector yomthombo ovulekile esheshayo enezindlela ze-ANN ezithuthukisiwe kanye nokuhlunga okuguquguqukayo; I-Chroma ihlose izimo zokusetshenziswa ezilula kanye nokuhlolwa ngolwazi olulula lonjiniyela; I-Vespa ihamba phambili ekusesheni nasekuhleleni okuhlanganisiwe okuhlanganisa amasimu ahlelekile, umbhalo kanye nama-vector; I-Deep Lake igxila kumasethi wedatha amaningi njengesithombe nevidiyo lapho ukuhlanganiswa okuqinile nezinhlaka ze-ML kubalulekile.

Ngesikhathi esifanayo, izizindalwazi zenhloso evamile seziqalile ukusebenzisa izici ze-vector kunokushiya isikhala ngokuphelele.. Ezinhlanganweni esezitshaliwe kakade ezitolo ze-SQL noma zemibhalo, lokhu kungaba yindlela esebenzayo yokwengeza usesho olunencazelo ngaphandle kokumisa uhlelo oluhlukile.

I-PostgreSQL enesandiso se-pgvector ingenye yezindlela ezithandwa kakhulu laphaI-Pgvector yethula uhlobo lwe-VECTOR olugcina amavekhtha obukhulu obungaguquki ngqo kumathebula e-Postgres futhi luveze opharetha bokufana kwebanga le-Euclidean, umkhiqizo wangaphakathi kanye nebanga le-cosine.

Lokho kusho ukuthi ungakha ithebula elifana ne-embeddings(id SERIAL PRIMARY KEY, vector VECTOR(768)), yifake ohlwini, bese usebenzisa imibuzo yefomu ethi “nginike amavekhtha ama-5 aseduze kakhulu ne-oda ngebanga le-L2”, konke ku-SQL ejwayelekile. Isandiso sisekela ama-index osayizi abaphezulu futhi sixhuma kahle kumafreyimu afana ne-LangChain.

Inzuzo enkulu ye-pgvector ubulula nokuhlanganiswa. Idatha yakho yokuthengiselana, amathebula okuhlaziya kanye nokushumeka konke kuhleli enjinini eyodwa, ngendaba eyodwa yokusekelayo kanye nokuphepha. Ukushintshana ukuthi i-Postgres ayakhelwe ngenhloso imisebenzi eminingi yamavektha ayizigidigidi, ngakho-ke ngezinga elikhulu kakhulu noma izidingo zokubambezeleka eziphansi kakhulu, i-vektha DB ezinikele ngokuvamile izoyidlula.

I-Elasticsearch kanye ne-OpenSearch nazo zingaguqulwa zibe izinhlelo eziqaphela ama-vector ngama-plugin e-k‑NN. Uma ithimba lakho selivele lisebenzisa iqoqo lokusesha lamalogi noma umbhalo ogcwele, ukuvumela amasimu e-vector kungase kwanele ukwenza i-prototype yokusesha kwe-semantic ngaphandle kokwakha kabusha. I-MongoDB nayo ijoyine lo mkhuba, ihlanganisa ukusesha kwe-vector ohlelweni lwayo oluqondiswe kumadokhumenti ukuze kusetshenziswe izimo ezilula.

Izinketho ezifakiwe nezilula: Izimo ze-VDB kanye ne-on-prem

Akuwona wonke amaphrojekthi adinga (noma angakwazi ukukhokhela) isizindalwazi sevektha esisabalalisiwe, sezinga lebhizinisiKwabasunguli abaningi namaqembu akha ama-MVP, amathuluzi ocwaningo noma izinhlelo zokusebenza ezikudivayisi, umtapo wolwazi olula, ofakiwe ukhanga kakhulu.

I-VDB iyisibonelo sesisombululo esilula kangaka: umtapo wolwazi we-C onekhanda kuphela osebenzisa ukusebenza kokusesha kwe-vector eyinhlokoIthunyelwa ngaphansi kwelayisensi ye-Apache 2.0 futhi ingafakwa ngqo kuzinhlelo zokusebenza ze-C noma ze-C++ ngaphandle kokuxhomekeka okungavamile ngaphandle kwama-pthreads angakhethwa wokuhlanganisa izintambo eziningi.

Isethi yezici eziyinhloko ihlanganisa lokho okudingwa yimikhiqizo eminingi yesigaba sokuqala. I-VDB isekela amamethrikhi amaningi okufana (i-cosine, i-Euclidean, umkhiqizo wangaphakathi), usesho olunemicu eminingi ukuze kusetshenziswe ama-CPU amaningi, ukuphikelela okuyisisekelo ukuze ukwazi ukulondoloza nokulayisha kabusha ama-indexes kusuka kudiski, kanye nokubopha okusemthethweni kwe-Python ukuze ukwazi ukukuhlanganisa ku-stack ejwayelekile ye-AI.

Ngenxa yokuthi kungokwesihloko kuphela, ukuhlanganiswa kulula kakhulu: faka izihloko kuphrojekthi yakho, uhlanganise, ukhiqize ukushumeka ngemodeli oyithandayo (i-OpenAI, i-Cohere, ama-Sentence Transformers, njll.), zifake ku-VDB ngama-ID noma i-metadata ehlobene, bese ubuza omakhelwane abaseduze abaphezulu lapho ukhonza izicelo.

Lo mklamo udlala kahle kakhulu ngokufakwa endaweni noma emaphethelweniUma wakha uhlelo lokusebenza lwesitayela se-LangChain + ChatGPT kodwa ufuna ukugcina konke ngemuva kwe-firewall yakho, ilabhulali efakiwe igwema ukuncika kwangaphandle kanye nokukhiya komthengisi. Kumadivayisi e-IoT noma ama-edge lapho ukubambezeleka kwamafu kungamukeleki, ukuba nesitolo se-vector esihlanganisiwe ku-binary yakho kuyinzuzo enkulu.

Yiqiniso, kukhona ukuhwebelana: I-VDB ayizami ukufaka esikhundleni se-DB yebhizinisi eliphelele. Ithembele ekusesheni okuqondile (okunamandla amakhulu) kunemagrafu e-ANN ayinkimbinkimbi noma ukulinganisa, ngakho-ke isikhathi semibuzo silinganiswa ngokuhambisana nosayizi wesethi yedatha. Kumashumi noma ngisho namakhulu ambalwa ezinkulungwane zamavektha, lokho kuvame ukwamukeleka, ikakhulukazi nge-multithreading; kumashumi ezigidi, cishe uzofinyelela imingcele ngaphandle kokuthi udwebe noma wethule ungqimba lwakho lokukhomba.

Usesho lwe-hybrid lomhlaba wangempela: ukujoyina ama-vector kanye ne-metadata

Empeleni, cishe zonke izimo zokusetshenziswa kokukhiqiza zihlanganisa ukufana kwevektha nezihlungi eziqinile ezimpawini ezihlelekileAbasebenzisi abavamile ukufuna "into efanayo kakhulu kulo lonke iqembu"; bafuna "okufanayo, kodwa futhi nokuhlonipha le mikhawulo".

Cabanga ngohlelo lokusebenza lokusesha izindlu lapho abasebenzisi bechaza khona umuzwa wekhaya – “isimanje saphakathi nekhulu leminyaka esinokukhanya okuningi kwemvelo” – kuyilapho kudinga nemingcele eqinile efana “namakamelo okulala amathathu”, “ngaphansi kuka-$800,000” kanye “nesifunda A”. Ukusesha okulula kwe-vector kungaletha ngenjabulo indlu enhle yaphakathi nekhulu leminyaka ebiza amadola ayizigidi ezimbili esifundeni sesikole esingafanele; izihlungi ze-SQL ezivamile azisoze zayiqonda indlela yokubhala isitayela.

Izinjini ezifana ne-AlloyDB ze-PostgreSQL zibonisa indlela yokubhekana nalokhu ngokuhlunga okusemgqeniI-AlloyDB ihlanganisa ukuhambisana kwe-Postgres nengqalasizinda enwebekayo ye-Google, ihlanganisa i-pgvector njengesandiso sekilasi lokuqala, futhi iyingeze ngenkomba yevektha esuselwe ku-ScaNN yokusesha okusheshayo kokufana.

Ukuhlunga kwayo okusemgqeni kusho ukuthi inkomba ye-vector kanye nezihlungi ze-metadata ze-SQL zisetshenziswa kuphasi elilodwaEsikhundleni sokwenza usesho lwe-vector, bese kuhlunga imigqa engafani ngemva kwalokho, i-AlloyDB ihlola imikhawulo yezinombolo neyezigaba njengoba idlula ku-vector index, igwema umsebenzi olahlekile kanye nezijeziso zokubambezeleka.

Umphumela wokugcina usesho oluhlanganisiwe olubuyisa izindlu ezifanela kokubili izintandokazi zobuhle kanye nezihlungi eziqinile ngaphakathi kwama-millisecond. Le ndlela ivame ukuhambisana nokuhweba nge-inthanethi (isitayela + intengo + isitoko), ukutholakala kokuqukethwe (isihloko + ulimi + isifunda), kanye nanoma yisiphi isizinda lapho "i-vibe" kufanele ihambisane nemithetho eqinile yebhizinisi.

Kusukela ekushumekeni kuya kuzinhlelo zokusebenza zokukhiqiza

Uma usukhethe indlela yokugcina izinto, ukugeleza kwezinga eliphezulu kwezici ezisekelwe kumavektha okwakha kuyavumelana ngokufanele, kungakhathaliseki ukuthi usebenzisa i-Milvus, i-Qdrant, i-PostgreSQL + i-pgvector, i-Elasticsearch k‑NN noma umtapo wolwazi olula njenge-VDB.

Okokuqala, udala ukushumeka kwe-corpus yakho. Ngombhalo, lokho kungaba amadokhumenti, izisekelo zolwazi, amathikithi, ama-imeyili noma izingodo zengxoxo; ngezithombe kanye nedatha ye-multimodal, ungasebenzisa amamodeli afanele ombono noma we-multimodal. Into ngayinye iba yi-vector kanye nanoma iyiphi imethadatha oyikhathalelayo.

Okulandelayo, ugcina okufakiwe esitolo se-vector esikhethiwe kanye nezihlonzi kanye ne-metadataKu-vector DB, lokhu ngokuvamile kusho ukudala iqoqo noma ithebula elinezinkambu ze-vector kanye ne-metadata; ku-VDB, kungaba yinkomba yememori esekelwa yizithombe eziku-disk.

Ngesikhathi sombuzo, ufaka okokufaka komsebenzisi ngemodeli efanayo bese ukhipha usesho lokufanaIsizindalwazi sibuyisela amavekhtha afana kakhulu aphezulu, bese ubheka izinto eziyisisekelo (amadokhumenti, imikhiqizo, izithombe) usebenzisa ama-ID azo noma imithwalo egciniwe.

Ku-RAG, udlulisela okuqukethwe okutholiwe njengomongo owengeziwe ku-LLM yakho. Ngezinhlelo zokuncoma, usebenzisa omakhelwane ngqo njengabazongenela ukulinganisa. Ukuze uthole ukuhlaziya noma ukuthola okungafani, ungahlanganisa amabanga nomakhelwane ukuze uqonde amaphethini kanye nezinto ezingaphandle.

Izizindalwazi ze-Vector zenza kube lula ukusebenzisa amamodeli okushumeka ngendlela eqinileEsikhundleni sokuphatha amafayela ngesandla noma ama-array angavamile, uthola ukuphathwa kwezinsiza ezifanele, ama-scaling knobs, izilawuli zokuphepha kanye nezilimi zemibuzo ezikuvumela ukuthi uveze ukufana okuyinkimbinkimbi + imibuzo yokuhlunga ngendlela ehlanzekile. Lezi zinkinga zokusebenza zifaka phakathi ukuqapha, ukulandelela kanye nokuphathwa kwama-LLM okukhiqiza kanye nama-vector, njengoba kuchaziwe ku izendlalelo zokubonwa kwe-AI.

Uma ihlanganiswe ne-AI ekhiqizayo, lesi sitaki senza okuhlangenwe nakho okuzwakala sengathi kwenziwe ngokwezifiso, okusekelwe kudatha yakho futhi okukwazi ukuthuthuka njengoba i-corpus yakho ikhula.Kungakhathaliseki ukuthi ukhetha i-DB esabalaliswe ngobuningi noma umtapo wolwazi olulula olutholakala ku-prem, izingcezu zomqondo - ukushumeka, izilinganiso zokufana, i-ANN noma usesho oluqondile, kanye nezihlungi zemethadatha - zihlala zifana futhi zakha umgogodla wezinhlelo zokusebenza ze-AI zanamuhla.

Njengoba izinhlelo ze-AI ziba nezingxoxo eziningi, ezisebenzisa izindlela eziningi futhi ezilambele umongo, indima yedathabheyisi ye-vector njengengqimba yememori ye-semantic izojula kakhulu.Ukuqonda ukuthi ama-vector agcinwa kanjani, afakwa kanjani ohlwini futhi aqhathaniswa kanjani kuba yikhono eliyinhloko kunoma ubani owakha izinhlelo zokusebenza ezingathi sína ngamamodeli olimi nombono.