g\u00f6mme<\/a> vekt\u00f6rleri bir sorgu vekt\u00f6r\u00fcne en yak\u0131n olan \u00f6\u011feleri bulur. Bu, ger\u00e7ek metin e\u015fle\u015fmesi yerine anlama dayal\u0131 aramay\u0131 m\u00fcmk\u00fcn k\u0131lar ve geleneksel veritabanlar\u0131n\u0131n verimli bi\u00e7imde hizmet edemedi\u011fi kullan\u0131m vakalar\u0131n\u0131 destekler.<\/p>\nTemel Mimari<\/h3>\n
Vector veritabanlar\u0131, \u00f6l\u00e7ekte benzerlik aramay\u0131 h\u0131zland\u0131rmak i\u00e7in yakla\u015f\u0131k en yak\u0131n kom\u015fu (ANN) algoritmalar\u0131\u2014HNSW, IVF, ScaNN\u2014kullan\u0131r. Tipik olarak metaveri \u00fczerinde filtrelemeyi (\u00f6rn. belirli bir kullan\u0131c\u0131dan benzer belgeleri al), hibrit aramay\u0131 (vector benzerli\u011fini anahtar kelime e\u015fle\u015fmesiyle birle\u015ftirmek) ve \u00e7ok kirac\u0131l\u0131 uygulamalar i\u00e7in ad alan\u0131 izolasyonunu destekler. \u0130ndeksleme stratejileri geri \u00e7a\u011f\u0131rma, gecikme, bellek kullan\u0131m\u0131 ve g\u00fcncelleme h\u0131z\u0131 aras\u0131nda de\u011fi\u015f toku\u015f yapar.<\/p>\n
\u00d6nde Gelen Sa\u011flay\u0131c\u0131lar ve A\u00e7\u0131k Kaynak Se\u00e7enekler<\/h3>\n
B\u00fcy\u00fck vector veritaban\u0131 \u00fcr\u00fcnleri aras\u0131nda Pinecone (y\u00f6netilen bulut), Weaviate (a\u00e7\u0131k kaynak + y\u00f6netilen), Qdrant (a\u00e7\u0131k kaynak + y\u00f6netilen), Milvus (a\u00e7\u0131k kaynak) ve Chroma say\u0131labilir. Yerle\u015fik veritabanlar\u0131 vector yetenekleri eklemi\u015ftir: PostgreSQL i\u00e7in pgvector, MongoDB Atlas Vector Search, Elasticsearch dense vector, Redis vector search. Do\u011fru se\u00e7im \u00f6l\u00e7e\u011fe, da\u011f\u0131t\u0131m modeline (bulut vs. on-prem) ve mevcut y\u0131\u011f\u0131nla entegrasyona ba\u011fl\u0131d\u0131r. \u00c7o\u011fu erken a\u015fama uygulama i\u00e7in pgvector veya y\u00f6netilen bir hizmet yeterlidir.<\/p>\n","protected":false},"excerpt":{"rendered":"
TLDR: Vector veritaban\u0131, verilerin y\u00fcksek boyutlu vector temsillerini (g\u00f6mme) depolama ve sorgulama i\u00e7in optimize edilmi\u015f bir veritaban\u0131d\u0131r; milyonlarca ila milyarlarca vector aras\u0131nda h\u0131zl\u0131 benzerlik aramay\u0131 m\u00fcmk\u00fcn k\u0131lar. Vector veritabanlar\u0131 modern yapay zeka uygulamalar\u0131, \u00f6zellikle RAG sistemleri i\u00e7in temel altyap\u0131d\u0131r. Vector Veritabanlar\u0131 Geleneksel Veritabanlar\u0131ndan Nas\u0131l Farkl\u0131 Geleneksel ili\u015fkisel ve belge veritabanlar\u0131 tam e\u015fle\u015fme veya anahtar kelime […]<\/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-11905","term","type-term","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/term\/11905","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=11905"}],"version-history":[{"count":1,"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/term\/11905\/revisions"}],"predecessor-version":[{"id":12322,"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/term\/11905\/revisions\/12322"}],"wp:attachment":[{"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/media?parent=11905"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/categories?post=11905"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}