Embedding (g\u00f6mme), verinin\u2014metin, g\u00f6r\u00fcnt\u00fc, ses, kod\u2014anlamsal anlam\u0131 s\u00fcrekli say\u0131sal uzayda yakalayan yo\u011fun vekt\u00f6r temsilidir. Embedding’ler modern yapay zekan\u0131n temelidir: anlamsal aramay\u0131, \u00f6nerileri, k\u00fcmelemeyi m\u00fcmk\u00fcn k\u0131lar ve LLM’lere<\/a> ve di\u011fer alt ak\u0131\u015f modellerine girdi olarak hizmet eder.<\/p>\n Bir embedding modeli (\u00f6rn. OpenAI text-embedding-3, Cohere Embed, Voyage AI, sentence-transformers gibi a\u00e7\u0131k kaynak modeller) girdi verisini al\u0131r ve sabit uzunlukta bir vekt\u00f6r \u00fcretir\u2014tipik olarak 384 ila 3.072 boyut. Anlamsal olarak benzer girdiler embedding uzay\u0131nda birbirine yak\u0131n vekt\u00f6rler \u00fcretir; bu, kosin\u00fcs benzerli\u011fi veya \u00d6klid mesafesiyle \u00f6l\u00e7\u00fcl\u00fcr. \u0130li\u015fkiler b\u00fcy\u00fck veri<\/a> k\u00fcmeleri \u00fczerinde model e\u011fitimi s\u0131ras\u0131nda \u00f6\u011frenilir.<\/p>\n Embedding’ler bir\u00e7ok \u00fcretim yapay zeka uygulamas\u0131n\u0131 destekler: anlamsal arama (anahtar kelimeler yerine anlamla belge bulma), \u00f6neri sistemleri (kullan\u0131c\u0131 tercihlerine benzer \u00f6\u011feleri bulma), k\u00fcmeleme ve s\u0131n\u0131fland\u0131rma (benzer \u00f6\u011feleri grupla), tekille\u015ftirme (yak\u0131n \u00e7ift i\u00e7eri\u011fi bulma) ve RAG<\/a> hatlar\u0131na ve alt ak\u0131\u015f ML modellerine girdi olarak. Bunlar temel altyap\u0131d\u0131r\u2014neredeyse her \u00fcretim yapay zeka sistemi y\u0131\u011f\u0131n\u0131n bir yerinde embedding kullan\u0131r.<\/p>\n Se\u00e7im kriterleri \u015funlard\u0131r: anlamsal kalite (MTEB gibi kar\u015f\u0131la\u015ft\u0131rmalarda \u00f6l\u00e7\u00fclen), boyutluluk (daha y\u00fcksek boyutlar daha fazla n\u00fcans yakalayabilir ancak depolama ve hesaplama maliyetli), alan uzmanl\u0131\u011f\u0131 (genel ama\u00e7l\u0131 vs. hukuk\/t\u0131p\/kod uzman\u0131 modeller), desteklenen diller (\u00e7ok dilli yetenek) ve fiyatland\u0131rma\/lisanslama. Bir\u00e7ok \u00fcretim ekibi farkl\u0131 i\u00e7erik t\u00fcrleri i\u00e7in birden \u00e7ok embedding modeli s\u00fcrd\u00fcr\u00fcr ve modeller \u00f6nemli \u00f6l\u00e7\u00fcde geli\u015fti\u011finde periyodik olarak yeniden embedding yapar.<\/p>\n","protected":false},"excerpt":{"rendered":" TLDR: Embedding (g\u00f6mme), verinin\u2014metin, g\u00f6r\u00fcnt\u00fc, ses, kod\u2014anlamsal anlam\u0131 s\u00fcrekli say\u0131sal uzayda yakalayan yo\u011fun vekt\u00f6r temsilidir. Embedding’ler modern yapay zekan\u0131n temelidir: anlamsal aramay\u0131, \u00f6nerileri, k\u00fcmelemeyi m\u00fcmk\u00fcn k\u0131lar ve LLM’lere ve di\u011fer alt ak\u0131\u015f modellerine girdi olarak hizmet eder. Embedding’ler Nas\u0131l \u00c7al\u0131\u015f\u0131r Bir embedding modeli (\u00f6rn. OpenAI text-embedding-3, Cohere Embed, Voyage AI, sentence-transformers gibi a\u00e7\u0131k kaynak modeller) […]<\/p>\n","protected":false},"author":4,"featured_media":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","format":"standard","meta":{"footnotes":""},"categories":[],"class_list":["post-11907","term","type-term","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/term\/11907","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/term"}],"about":[{"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/types\/term"}],"author":[{"embeddable":true,"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/comments?post=11907"}],"version-history":[{"count":1,"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/term\/11907\/revisions"}],"predecessor-version":[{"id":12321,"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/term\/11907\/revisions\/12321"}],"wp:attachment":[{"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/media?parent=11907"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/categories?post=11907"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Embedding’ler Nas\u0131l \u00c7al\u0131\u015f\u0131r<\/h3>\n
Kullan\u0131m Alanlar\u0131<\/h3>\n
Embedding Modeli Se\u00e7imi<\/h3>\n