Artificial intelligence (AI) iri kushandura magadzirirwo atinoita uye kuongorora data. Uye, vector dhatabhesi ndeimwe yemidziyo yekutanga inotyaira iyi shanduko.
Aya dhatabhesi anoshanda zvakanyanya mukuchengetedza uye kutora yakakwirira-dimensional data inomiririra.
Ivo vane mukana wekuita basa rakakosha mukubudirira kweAI maapplication akadai seyakasikwa mutauro kugadzirisa, kucherechedzwa kwemifananidzo, uye kurudziro masisitimu.
Mune ino positi, isu tichatarisa inonakidza ndima yevector dhatabhesi muAI uye nei ivo vakanyanya kukosha kune data masayendisiti uye nyanzvi dzekudzidza muchina.
Nei Relational Databases isina kukwana kune AI Zvikumbiro
Isu tinowanzo chengetedza uye kutora data tichishandisa echinyakare hukama dhatabhesi. Nekudaro, aya dhatabhesi haagare akanyatsokodzera-yepamusoro-dimensional data inomiririra, zvinova zvinowanzoitika muhuwandu hweAI maapplication.
Kugadzirisa huwandu hukuru hwe data isina kurongeka iyo inowanzo shandiswa muAI inogona kunetsa nekuda kweaya dhatabhesi 'akarongeka hunhu.
Nyanzvi dzaida kudzivirira kunonoka uye kusashanda kutsvaga. Saka, kuti vakunde matambudziko aya, vakashandisa zvigadziriso zvakaita sekubata kumeso zvimiro zvedata. Nekudaro, iyi yaive nzira inopedza nguva uye yekukanganisa.
Imwe nzira inoshanda kwazvo yekuchengetedza uye kudzoreredza data yakakwira-dimensional yabuda nekukwira kwevector dhatabhesi. Nenzira iyi, zvinokwanisika kuve neakawanda akakwenenzverwa uye akabudirira maAI maapplication.
Zvino, ngationei kuti aya vector dhatabhesi anoshanda sei.
Chii chaizvo chinonzi vector databases?
Vector dhatabhesi ndeakasarudzika dhatabhesi anoitirwa kuchengetedza uye kubata huwandu hukuru hwepamusoro-dimensional data nenzira yemavheji.
Vectors inomiririra data yemasvomhu inotsanangura zvinhu zvichienderana nehunhu hwavo hwakasiyana kana hunhu.
Vector yega yega inomiririra imwe data poindi, senge izwi kana mufananidzo, uye inoumbwa nemuunganidzwa wezvakakosha zvinotsanangura hunhu hwayo hwakawanda. Izvi zvakasiyana-siyana dzimwe nguva zvinozivikanwa se "maitiro" kana "zviyero."
Mufananidzo, semuenzaniso, unogona kumiririrwa sevheta yezvakakosha zvemapikisi, asi mutsara wese unogona kumiririrwa sevheta yemazwi akaiswa.
Vector dhatabhesi dzinoshandisa nzira dzekutarisa kurerutsira kuwanikwa kwemavheta akafanana nechero mubvunzo vector. Izvi zvinonyanya kubatsira mu machine learning maapplication, sekutsvaga kwakafanana kunowanzo shandiswa kuwana anofananidzwa mapoinzi edata kana kugadzira mazano.
Inner Workings yeVector Databases
Vector dhatabhesi rinoshandiswa kuchengetedza uye indexer akakwira-dimensional mavheti anogadzirwa nehunyanzvi senge kudzidza zvakadzika. Aya mavheji anomiririra nhamba yezvinhu zvakaoma zve data izvo zvinoshandurirwa munzvimbo yakaderera-dimensional apo ichichengetedza ruzivo rwakakosha kuburikidza nehunyanzvi hwekumisikidza.
Saka, vector dhatabhesi akavakwa kuti agadzirise iyo chaiyo dhizaini yekumisikidza vector, uye vanoshandisa indexing algorithms kunyatsotsvaga uye kutora mavheji zvichienderana nekufanana kwavo kune mubvunzo vector.
Chinoshanda sei?
Vector dhatabhesi inoshanda zvakafanana kumabhokisi emashiripiti ekuchengeta uye kuronga zvinhu zvakaoma data.
Vanoshandisa nzira dzePQ neHNSW kuona uye kuwana ruzivo rwakakwana nekukurumidza. PQ inoshanda zvakafanana kune Lego zvidhinha, inokwenenzvera mavheji kuita zvidimbu zvidiki kubatsira mukutsvaga akafanana.
HNSW, kune rumwe rutivi, inogadzira dandemutande remanongedzo kuti arongedze mavheji muhurongwa, zvichiita kuti kufamba uye kutsvaga kuve nyore. Dzimwe sarudzo dzekugadzira, dzakadai sekuwedzera uye kubvisa mavheji kuti aone kufanana uye mutsauko, anotsigirwawo nevector dhatabhesi.
Vector Databases Anoshandiswa sei muAI?
Vector dhatabhesi vane mukana mukuru munzvimbo ye chakagadzirwa njere. Ivo vanotibatsira nemazvo kubata huwandu hukuru hwe data uye kutsigira mashandiro akaomarara akadai sekutsvaga kufanana uye vector arithmetic.
Vakave maturusi akakosha mumhando dzakasiyana dzekushandisa. Izvi zvinosanganisira kugadzirwa kwemutauro wechisikigo, kucherechedzwa kwemifananidzo, uye masisitimu ekurudziro. MaVector embeddings, semuenzaniso, anoshandiswa mukugadzirisa mutauro wechisikigo kuti anzwisise zvinorehwa uye mamiriro echinyorwa, zvichibvumira mhinduro yakarurama uye yakakodzera yekutsvaga.
Vector dhatabhesi mukuzivikanwa kwemifananidzo inogona kutsvaga mifananidzo yakafanana zvakanaka, kunyangwe mumaseti makuru. Vanogonawo kupa zvinhu zvinofananidzwa kana ruzivo kune vatengi zvichibva pane zvavanoda uye maitiro mumasisitimu ekurudziro.
Maitiro Akanyanya Ekushandisa Vector Databases muArtificial Intelligence
Kuti utange, iwo mavector ekuisa anofanirwa kufanogadziridzwa uye akajairwa asati achengetwa mudhatabhesi. Izvi zvinogona kuwedzera kurongeka uye kuita kwevector yekutsvaga.
Chechipiri, iyo yakakodzera indexing algorithm inofanirwa kusarudzwa zvichienderana neyega yekushandisa kesi uye kugovera data. maalgorithms akasiyana-siyana ane kutengeserana-kusiyana-siyana pakati pekururama nekumhanya, uye kusarudza iyo yakakodzera inogona kuva nesimba rakakura pakuita kwekutsvaga.
Chechitatu, kuvimbisa kuita kwakaringana, iyo vector dhatabhesi inofanirwa kuongororwa uye kuchengetedzwa nguva dzose. Izvi zvinosanganisira kudzokorodza dhatabhesi sezvinodiwa, kunyatsogadzirisa maparameter, uye kuongorora mashandiro ekutsvaga kutsvaga nekugadzirisa chero matambudziko.
Chekupedzisira, kuti uwedzere kugona kweAI maapplication, zvinokurudzirwa kushandisa vector dhatabhesi inotsigira zvakaomesesa zvakaita sevector arithmetic uye kufanana kutsvaga.
Sei Uchifanira Kushandisa Vector Database?
Icho chinonyanya kufanana chinangwa chekushandisa vector dhatabhesi ndeyekutsvaga vector mukugadzira. Kufanana kwezvinhu zvakawanda kumubvunzo wekutsvaga kana chinhu chemusoro chinofananidzwa mumhando iyi yekutsvaga. Vector database ine mukana wekuenzanisa kufanana kwezvinhu izvi kuti uwane machisi ari padyo nekushandura chinhu chemusoro kana kubvunza kuita vheki uchishandisa imwecheteyo ML yekumisikidza modhi.
Izvi zvinoburitsa mhedzisiro chaiyo uchidzivirira zvisina basa mhedzisiro inogadzirwa neyakajairwa kutsvaga matekinoroji.
Image, Audio, Vhidhiyo Kufanana Kutsvaga
Mifananidzo, mimhanzi, vhidhiyo, uye rumwe ruzivo rusina kurongeka zvinogona kunetsa kuisa mumapoka uye kuchengeta mune yakajairwa dhatabhesi. Vector dhatabhesi mhinduro yakanaka kune iyi sezvo vachigona kutsvaga zvinhu zvinofananidzwa nekukurumidza kunyangwe mumaseti akakura. Iyi nzira haidi munhu data tagging kana kuisa mazita uye inokwanisa kukurumidza kutsvaga machisi ari pedyo zvichienderana nezvibodzwa zvakafanana.
Injini dzeChinzvimbo uye Kurudziro
Vector dhatabhesi zvakare yakakodzerwa kuti ishandiswe mune chinzvimbo uye kurudziro masisitimu. Dzinogona kushandiswa kukurudzira zvinhu zvakafanana nezvakatengwa kare kana chinhu chiripo icho mutengi ari kutarisa.
Panzvimbo pekutsamira pakusefa kwakabatana kana mazita ekukurumbira, kutepfenyura midhiya masevhisi anogona kukwidziridza nziyo dzemushandisi kupa mazano anonyatsoenderana akagadzirirwa munhu. Vanogona kutsvaga zvigadzirwa zvakafanana zvichienderana nemachisi ari pedyo.
Semantic kutsvaga
Semantic yekutsvaga ishoko rakasimba uye gwaro rekutsvaga chishandiso chinopfuura zvakajairwa mazwi ekutsvaga. Zvinoreva uye mamiriro etambo dzemavara, mitsara, uye zvinyorwa zvese zvinogona kunzwisiswa nekushandisa vector dhatabhesi kuchengetedza uye index vector embeddings kubva Natural. Mutauro Processing mhando.
Saka, vashandisi vanozokwanisa kuwana zvavanoda nekukurumidza pasina kunzwisisa kuti iyo data yakakamurwa sei.
Technologies yeVector Databases
Kune akasiyana vector dhatabhesi tekinoroji iripo, imwe neimwe iine yayo seti yezvakanakira uye zvayakaipira.
pine koni, Faiss, Annoy, Milvus, uye Hnswlib ndezvimwe zvezvingangofarirwa zvakanyanya.
pine koni
Iyo igore-yakavakirwa vector database. Iwe unogona kugadzira chaiyo-nguva yekufanana yekutsvaga maapplication. Inogonesa vashandisi kuchengetedza uye kuongorora yakakwira-dimensional vector embeddings ine millisecond latencies.
Izvi zvinoita kuti ive yakakodzera kune zvikumbiro senge kurudziro masisitimu, pikicha uye vhidhiyo yekutsvaga, uye yakasikwa mutauro kugadzirisa.
Pinecone's primary features zvinosanganisira otomatiki indexing, real-time updates, query auto-tuning, uye REST API yekudyidzana kuri nyore nemaitiro azvino. Chivakwa chayo chakagadzirirwa scalability uye kusimba. Iwe unogona kubata zviri nyore huwandu hwe data uchichengetedza kuwanikwa kwakanyanya.
Faiss
Iyo Facebook yakavhurika-sosi pasuru inopa yekucheka-kumucheto mashandisirwo eiyo indexing uye kutsvaga algorithms kune makuru-scale vectors.
Inotsigira akati wandei vector maitiro ekutsvaga. Imwe yemabhenefiti ayo ekutanga kumhanya kwayo uye scalability, iyo inobvumira kutsvaga nekukurumidza kunyangwe mumaseti ane mabhiriyoni emavheji.
Annoy
Kutsamwisa, kune rumwe rutivi, iC ++ raibhurari yakavakirwa yakakwirira-dimensional inofungidzirwa yepedyo yekutsvaga muvakidzani. Zviri nyore kushandisa uye kushandisa iyo isina kurongeka yemuti tekinoroji nekukurumidza.
Kutsamwisa iraibhurari diki yekurangarira tsoka inokodzera kushandiswa mune zviwanikwa-zvine dzvinyiriro.
Milvus
Milvus inzvimbo yemahara uye yakavhurika-sosi vector dhatabhesi yekuchengetedza uye kutsvaga mahombe-mavheji. Iyo inotsigira akasiyana siyana ekunongedza maitiro, anosanganisira IVF neHNSW, uye anogona kubata zviri nyore mamirioni emavheji.
Kugona kwayo kweGPU kukwidziridza, iyo inogona kukurumidzira zvakanyanya maitiro ekutsvaga, ndeimwe yeayo akasarudzika maficha.
Zviri nyore sarudzo yakanakisa kana uchifunga kusarudza chigadzirwa chevector dhatabhesi.
Hnswlib
Hnswlib imwezve yakavhurika-sosi raibhurari inopa hierarchical inofambika diki-nyika network yekukurumidza indexing uye kutsvaga akakwira-dimensional vectors.
Yakanakira mamiriro ezvinhu apo iyo vector nzvimbo inogara ichichinja, uye inopa incremental indexing kuchengetedza index kusvika ikozvino nemavheji matsva. Iyo zvakare inochinjika zvakanyanya, ichibvumira vashandisi kugadzirisa zvakanaka chiyero chechokwadi uye nekumhanya.
Zvinogoneka Drawbacks
Nepo vector dhatabhesi vane akawanda mabhenefiti, ivo vanewo zvakakomba zvakashata. Imwe inogoneka kunetseka ndeye yakakwira yakawanda yekuchengetedza inodiwa kubata vector embeddings.
Uyezve, vector dhatabhesi inogona kunetsekana nemhando dze data, senge mipfupi kana yakanyanya kunyanya kubvunza mibvunzo. Chekupedzisira, kumisikidza nekugadzirisa aya dhatabhesi kunogona kusanganisira hunyanzvi hukuru, zvichiita kuti dzisawanikwe kune vamwe vashandisi.
Chii chinonzi The Next Level?
Kune akasiyana siyana anogoneka ekuvandudza pakatarisana sezvo vector dhatabhesi iri kuramba ichishanduka. Imwe nharaunda iyo kufambira mberi kwakanyanya kungaitwa kuri mukugadzirwa kwemamodhi akanyanya uye anoshanda eNLP.
Izvi zvinogona kutungamira kune yakavandudzwa mavheti ekumisikidza anotora zvinoreva uye mamiriro ezvinyorwa zvakanyatsojeka, zvichiita kuti tsvakiridzo dzinyanye kuve dzakarurama uye dzakakodzera.
Imwe nzvimbo yekufambira mberi inogona kunge iri yepamusoro-soro algorithms yezviyero uye kurudziro injini, ichibvumira kune yakatonyanya kurongeka uye yakanangwa kurudziro.
Uyezve, kufambira mberi muhunyanzvi, senge maGPU uye maCPU akasarudzika, anogona kubatsira kuwedzera kukurumidza uye kugona kweVector database mashandiro. Nenzira iyi vanogona kuwanikwa zvakanyanya kune akasiyana siyana evashandisi uye maapplication.
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