{"id":33865,"date":"2019-10-31T21:55:06","date_gmt":"2019-10-31T18:55:06","guid":{"rendered":"https:\/\/prohoster.info\/blog\/microsoft-otkryl-kod-biblioteki-vektornogo-poiska-ispolzuemoj-v-bing\/"},"modified":"2019-10-31T21:55:06","modified_gmt":"2019-10-31T18:55:06","slug":"microsoft-otkryl-kod-biblioteki-vektornogo-poiska-ispolzuemoj-v-bing","status":"publish","type":"post","link":"https:\/\/prohoster.info\/ro\/blog\/news\/microsoft-otkryl-kod-biblioteki-vektornogo-poiska-ispolzuemoj-v-bing","title":{"rendered":"Microsoft a deschis codul bibliotecii de c\u0103utare vectorial\u0103 utilizat\u0103 \u00een Bing","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Compania Microsoft a publicat<\/a><\/noindex> codurile surs\u0103 ale bibliotecii de \u00eenv\u0103\u021bare automat\u0103 SPTAG<\/a><\/noindex> (Space Partition Tree And Graph) cu implementarea algoritmului de c\u0103utare aproximativ\u0103 a vecinului cel mai apropiat<\/a><\/noindex>. Biblioteca a fost dezvoltat\u0103<\/a><\/noindex> \u00een divizia de cercetare Microsoft Research \u0219i centrul de dezvoltare a tehnologiilor de c\u0103utare (Microsoft Search Technology Center). \u00cen practic\u0103, SPTAG este utilizat \u00een motorul de c\u0103utare Bing pentru a determina cele mai relevante rezultate \u00een func\u021bie de contextul interog\u0103rilor de c\u0103utare. Codul este scris \u00een C++ \u0219i se r\u0103sp\u00e2nde\u0219te<\/a><\/noindex> sub licen\u021ba MIT. Este disponibil\u0103 o compilare pentru Linux \u0219i Windows. Exist\u0103 un wrapper pentru limbajul Python.<\/p>\n

De\u0219i ideile de aplicare a depozitelor vectoriale \u00een motoarele de c\u0103utare circul\u0103 de ceva timp, implementarea lor \u00een practic\u0103 este \u00eempiedicat\u0103 de consumul mare de resurse al opera\u021biunilor cu vectori \u0219i de limit\u0103rile \u00een scalabilitate. Combinarea metodelor de \u00eenv\u0103\u021bare profund\u0103 cu algoritmii de c\u0103utare aproximativ\u0103 a vecinului cel mai apropiat a permis cre\u0219terea performan\u021bei \u0219i scalabilit\u0103\u021bii sistemelor vectoriale la un nivel acceptabil pentru marile motoare de c\u0103utare. De exemplu, \u00een Bing, pentru un indice vectorial de peste 150 de miliarde de vectori, timpul necesar pentru a ob\u021bine cele mai relevante rezultate este de 8 ms.<\/p>\n

Biblioteca include instrumente pentru construirea indicelui \u0219i organizarea c\u0103ut\u0103rii vectorilor, precum \u0219i un set de unelte pentru \u00eentre\u021binerea unui sistem de c\u0103utare online distribuit, care acoper\u0103 colec\u021bii foarte mari de vectori. Se propun<\/a><\/noindex> urm\u0103toarele module: index builder pentru indexare, searcher pentru c\u0103utare folosind indexul, distribuit \u00eentr-un cluster de mai multe noduri, un server pentru a rula handleri pe noduri, Aggregator pentru a combina mai multe servere \u00eentr-un \u00eentreg \u0219i un client pentru a trimite cereri. Este sus\u021binut\u0103 introducerea de noi vectori \u00een index \u0219i \u0219tergerea vectorilor \u00een timp real.<\/p>\n

Biblioteca presupune c\u0103 datele procesate \u0219i prezentate \u00een colec\u021bie sunt organizate sub form\u0103 de vectori conecta\u021bi, care pot fi compara\u021bi pe baza distan\u021belor euclidiene<\/a><\/noindex> (L2) sau cosinusale<\/a><\/noindex> Atunci c\u00e2nd se efectueaz\u0103 o interogare de c\u0103utare, se returneaz\u0103 vectorii a c\u0103ror distan\u021b\u0103 fa\u021b\u0103 de vectorul ini\u021bial este minim\u0103. \u00cen SPTAG sunt disponibile dou\u0103 metode de organizare a spa\u021biului vectorial: SPTAG-KDT (arbore K-dimensional (kd-tree<\/a><\/noindex>) \u0219i graf_sectiunilor_relative<\/a><\/noindex>) \u0219i SPTAG-BKT (copac k-medii (copacul k-medii<\/a><\/noindex> \u0219i graf_sectiunilor_relative). Prima metod\u0103 necesit\u0103 mai pu\u021bine resurse la lucrul cu indexul, \u00een timp ce a doua demonstreaz\u0103 o precizie mai mare a rezultatelor c\u0103ut\u0103rii \u00een colec\u021bii foarte mari de vectori.<\/p>\n

\u00cen acest context, c\u0103utarea vectorial\u0103 nu se limiteaz\u0103 la text \u0219i poate fi aplicat\u0103 informa\u021biilor multimedia \u0219i imaginilor, precum \u0219i \u00een sistemele de generare automat\u0103 a recomand\u0103rilor. De exemplu, \u00eentr-unul dintre prototipuri bazate pe cadrul PyTorch, a fost implementat un sistem vectorial pentru c\u0103utarea similarit\u0103\u021bii obiectelor \u00een imagini, construit folosind date din mai multe colec\u021bii de referin\u021b\u0103 cu imagini de animale, pisici \u0219i c\u00e2ini, care au fost transformate \u00een seturi de vectori. C\u00e2nd o imagine de intrare este primit\u0103 pentru c\u0103utare, aceasta este transformat\u0103 cu ajutorul unui model de \u00eenv\u0103\u021bare automat\u0103 \u00eentr-un vector, pe baza c\u0103ruia, folosind algoritmul SPTAG, se selecteaz\u0103 vectorii cei mai asem\u0103n\u0103tori din index \u0219i, ca rezultat, sunt returnate imaginile asociate.<\/p>\n

<\/a><\/noindex><\/p>\n

Sursa: opennet.ro<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Microsoft \u043e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043b\u0430 \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u0435 \u0442\u0435\u043a\u0441\u0442\u044b \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f SPTAG (Space Partition Tree And Graph) \u0441 \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0435\u0439 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0430 \u043f\u0440\u0438\u0431\u043b\u0438\u0437\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0433\u043e \u043f\u043e\u0438\u0441\u043a\u0430 \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0435\u0433\u043e \u0441\u043e\u0441\u0435\u0434\u0430. \u0411\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0430\u043d\u0430 \u0432 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044c\u0441\u043a\u043e\u043c \u043f\u043e\u0434\u0440\u0430\u0437\u0434\u0435\u043b\u0435\u043d\u0438\u0438 Microsoft Research \u0438 \u0446\u0435\u043d\u0442\u0440\u0435 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u043f\u043e\u0438\u0441\u043a\u043e\u0432\u044b\u0445 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0439 (Microsoft Search Technology Center). \u041d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435 SPTAG \u043f\u0440\u0438\u043c\u0435\u043d\u044f\u0435\u0442\u0441\u044f \u0432 \u043f\u043e\u0438\u0441\u043a\u043e\u0432\u043e\u0439 \u0441\u0438\u0441\u0442\u0435\u043c\u0435 Bing \u0434\u043b\u044f \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044f \u043d\u0430\u0438\u0431\u043e\u043b\u0435\u0435 \u0440\u0435\u043b\u0435\u0432\u0430\u043d\u0442\u043d\u044b\u0445 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u0441 \u0443\u0447\u0451\u0442\u043e\u043c \u043a\u043e\u043d\u0442\u0435\u043a\u0441\u0442\u0430 \u043f\u043e\u0438\u0441\u043a\u043e\u0432\u044b\u0445 \u0437\u0430\u043f\u0440\u043e\u0441\u043e\u0432. 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