{"id":523,"date":"2025-12-14T20:00:48","date_gmt":"2025-12-14T17:00:48","guid":{"rendered":"https:\/\/neku.ai\/dense-retrieval-vektor-arama\/"},"modified":"2025-12-14T20:01:11","modified_gmt":"2025-12-14T17:01:11","slug":"dense-retrieval-vektor-arama","status":"publish","type":"post","link":"https:\/\/neku.ai\/en\/dense-retrieval-vektor-arama\/","title":{"rendered":"Dense Retrieval ile RAG Sistemlerinde H\u0131zl\u0131 ve Do\u011fru Bilgi Getirme"},"content":{"rendered":"

Dense retrieval nedir<\/h1>\n
\n

Giri\u015f<\/strong><\/h3>\n

Dense retrieval, bilgi getirme (information retrieval) sistemlerinde metin ve dok\u00fcmanlar\u0131 vekt\u00f6r temelli olarak e\u015fle\u015ftiren modern bir y\u00f6ntemdir. \u00d6zellikle RAG (Retrieval-Augmented Generation) mimarilerinde, do\u011fru bilgiye y\u00fcksek h\u0131zla ve ba\u011flama uygun bi\u00e7imde eri\u015filmesini sa\u011flar. Geleneksel anahtar kelime aramalar\u0131ndan farkl\u0131 olarak, dense retrieval semantik benzerli\u011fi esas al\u0131r ve yapay zeka modellerinin ba\u011flam anlay\u0131\u015f\u0131n\u0131 do\u011frudan arama s\u00fcrecine dahil eder.<\/p>\n

\n

Dense retrieval nedir tan\u0131m\u0131<\/strong><\/h3>\n

Dense retrieval, dok\u00fcman veya sorgu metinlerini y\u00fcksek boyutlu vekt\u00f6r temsilcilerine \u00e7eviren modellerin (genellikle transformer tabanl\u0131 embedding modelleri) kullan\u0131ld\u0131\u011f\u0131 bir bilgi getirme yakla\u015f\u0131m\u0131d\u0131r. Bu y\u00f6ntemde arama i\u015flemi, vekt\u00f6r uzay\u0131nda en yak\u0131n kom\u015fular\u0131 bulmak \u015feklinde ger\u00e7ekle\u015fir. B\u00f6ylece sistem yaln\u0131zca kelime benzerli\u011fi de\u011fil, anlam benzerli\u011fi \u00fczerinden sonu\u00e7 \u00fcretir.<\/p>\n

\n

dense retrieval nas\u0131l \u00e7al\u0131\u015f\u0131r<\/strong><\/h3>\n

Dense retrieval sistemi, a\u015fa\u011f\u0131daki ana ad\u0131mlar \u00fczerinden \u00e7al\u0131\u015f\u0131r: <\/p>\n

    \n
  1. Dok\u00fcmanlar embedding model kullan\u0131larak vekt\u00f6rel forma d\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcr. <\/li>\n
  2. Sorgu metni ayn\u0131 modelle encode edilir. <\/li>\n
  3. Sorgu vekt\u00f6r\u00fc ile dok\u00fcman vekt\u00f6rleri aras\u0131nda benzerlik \u00f6l\u00e7\u00fcl\u00fcr (\u00f6rne\u011fin cosine similarity). <\/li>\n
  4. En y\u00fcksek benzerlik skoruna sahip i\u00e7erikler geri d\u00f6nd\u00fcr\u00fcl\u00fcr. <\/li>\n<\/ol>\n

    Bu ak\u0131\u015f, RAG mimarilerinde soruya uygun bilgi kayna\u011f\u0131n\u0131 dinamik olarak se\u00e7mek i\u00e7in kullan\u0131l\u0131r.<\/p>\n

    \n

    Temel parametreler ve ayarlar<\/strong><\/h3>\n
      \n
    • Embedding boyutu:<\/strong> Modelin ifadeyi ne kadar detayl\u0131 temsil edece\u011fini belirler. Genelde 384\u20131024 boyut aral\u0131\u011f\u0131 yeterlidir. <\/li>\n
    • Benzerlik metri\u011fi:<\/strong> Cosine veya dot product y\u00f6ntemleri tercih edilir. <\/li>\n
    • Indexer yap\u0131s\u0131:<\/strong> Faiss, HNSW veya ScaNN gibi k\u00fct\u00fcphaneler y\u00fcksek performansl\u0131 yak\u0131n kom\u015fu aramalar\u0131 i\u00e7in kullan\u0131l\u0131r. <\/li>\n
    • Batch yap\u0131land\u0131rmas\u0131:<\/strong> Dok\u00fcman vekt\u00f6rle\u015ftirme s\u00fcrecinde GPU haf\u0131zas\u0131na uygun batch boyutu performans\u0131 etkiler.<\/li>\n<\/ul>\n
      \n

      S\u0131k yap\u0131lan hatalar ve ka\u00e7\u0131nma y\u00f6ntemleri<\/strong><\/h3>\n
        \n
      • Yanl\u0131\u015f model se\u00e7imi:<\/strong> Domain\u2019e \u00f6zel embedding modelleri kullan\u0131lmad\u0131\u011f\u0131nda semantik kaymalar olu\u015fur. <\/li>\n
      • Normalize edilmemi\u015f vekt\u00f6rler:<\/strong> Benzerlik hesaplar\u0131nda tutars\u0131z sonu\u00e7lara yol a\u00e7ar. <\/li>\n
      • A\u015f\u0131r\u0131 b\u00fcy\u00fck indeksler:<\/strong> \u00d6nbellekleme ve disk eri\u015fim maliyetini art\u0131r\u0131r, segmentasyon gerekir. <\/li>\n<\/ul>\n

        Do\u011fru model se\u00e7imi, optimize edilmi\u015f index mimarisi ve d\u00fczenli yeniden e\u011fitim s\u00fcre\u00e7leri bu hatalardan ka\u00e7\u0131nmak i\u00e7in temel y\u00f6ntemlerdir.<\/p>\n

        \n

        Ger\u00e7ek sistemlerde uygulama \u00f6rnekleri<\/strong><\/h3>\n

        Kurum i\u00e7i dok\u00fcman arama motorlar\u0131, sohbet tabanl\u0131 bilgi tabanlar\u0131 ve m\u00fc\u015fteri destek asistanlar\u0131 dense retrieval\u2019i yayg\u0131n olarak kullan\u0131r. \u00d6rne\u011fin bir SAP entegrasyon s\u00fcrecinde, dok\u00fcmantasyon i\u00e7erisinden i\u015flem ad\u0131mlar\u0131n\u0131n semantik olarak \u00e7a\u011fr\u0131lmas\u0131 dense retrieval arac\u0131l\u0131\u011f\u0131yla m\u00fcmk\u00fcn hale gelir. Bu yakla\u015f\u0131m hem dok\u00fcman i\u015fleme verimlili\u011fini hem de operasyonel h\u0131z\u0131n\u0131 art\u0131r\u0131r.<\/p>\n

        \n

        Teknik a\u00e7\u0131klama (derin seviye)<\/strong><\/h3>\n

        Dense retrieval mimarisi tipik olarak iki bile\u015fenden olu\u015fur: encoder modeller<\/strong> ve vekt\u00f6r indeksleme mekanizmas\u0131<\/strong>. Encoder modeli, sorgu ve dok\u00fcman\u0131 ayn\u0131 uzaya ta\u015f\u0131yan iki ayr\u0131 a\u011f (dual encoder) i\u00e7erir. Her encoder metni y\u00fcksek boyutlu vekt\u00f6r \u015feklinde kodlar. Ard\u0131ndan bu vekt\u00f6rler HNSW veya IVF indeksleri kullan\u0131larak saklan\u0131r.
        \nSorgu geldi\u011finde ayn\u0131 encoder ile vekt\u00f6r \u00fcretilir ve indeks \u00fczerinden en yak\u0131n kom\u015fular tespit edilir. Bu s\u00fcre\u00e7 milisaniyeler seviyesinde sonu\u00e7 sa\u011flar. RAG yap\u0131lar\u0131nda, elde edilen i\u00e7erikler dil modeline “grounding” i\u00e7in eklenir.<\/p>\n

        \n

        \u0130\u015fletmeler i\u00e7in neden kritiktir<\/strong><\/h3>\n
          \n
        • Performans:<\/strong> Y\u00fcksek h\u0131zl\u0131 semantik arama, karar sistemlerinde yan\u0131t s\u00fcresini d\u00fc\u015f\u00fcr\u00fcr. <\/li>\n
        • G\u00fcvenilirlik:<\/strong> Bilgi taban\u0131 tutarl\u0131l\u0131\u011f\u0131 ve geri \u00e7a\u011f\u0131rma oran\u0131 artar. <\/li>\n
        • Maliyet:<\/strong> Gereksiz sorgu karmas\u0131 ve dok\u00fcman y\u00fcklemesi azal\u0131r. <\/li>\n
        • \u00d6l\u00e7ekleme:<\/strong> B\u00fcy\u00fck veri setlerinde indeksleme verimli hale gelir. <\/li>\n
        • Otomasyon:<\/strong> S\u00fcre\u00e7 ak\u0131\u015flar\u0131 n8n veya SAP gibi sistemlerde dinamik bilgilendirilebilir. <\/li>\n
        • Karar alma:<\/strong> Do\u011fru veriye eri\u015fim analitik do\u011frulu\u011fu art\u0131r\u0131r. <\/li>\n
        • Operasyonel verimlilik:<\/strong> \u00c7al\u0131\u015fanlar\u0131n bilgi arama s\u00fcreci h\u0131zlan\u0131r.<\/li>\n<\/ul>\n
          \n

          Bu kavram NeKu.AI i\u00e7inde nas\u0131l uygulan\u0131r<\/strong><\/h3>\n

          NeKu.AI, bilgi taban\u0131 i\u00e7inde grounding mimarisini g\u00fc\u00e7lendirmek i\u00e7in dense retrieval yakla\u015f\u0131m\u0131n\u0131 kullan\u0131r. Sistem, kurumsal dok\u00fcmanlar\u0131 vekt\u00f6r format\u0131na \u00e7evirir ve sorgulara semantik olarak en uygun i\u00e7erikleri se\u00e7er. Bu yap\u0131, otomasyon orkestrasyon s\u00fcre\u00e7lerinde n8n entegrasyonlar\u0131yla birle\u015ferek RAG temelli karar mekanizmalar\u0131n\u0131n do\u011frulu\u011funu art\u0131r\u0131r.<\/p>\n

          \n

          AI geli\u015ftiricileri, veri m\u00fchendisleri i\u00e7in ger\u00e7ek bir senaryo<\/strong><\/h3>\n
            \n
          1. Sorun:<\/strong> Bir kurumun m\u00fc\u015fteri taleplerine yan\u0131t veren chatbot\u2019u, dok\u00fcman i\u00e7eri\u011fini do\u011fru \u015fekilde \u00e7a\u011f\u0131rm\u0131yor. <\/li>\n
          2. Ba\u011flam:<\/strong> Dok\u00fcmanlar farkl\u0131 formatlarda ve metin i\u00e7inde y\u00fcksek dil \u00e7e\u015fitlili\u011fi var. <\/li>\n
          3. Kavram\u0131n uygulanmas\u0131:<\/strong> Dense retrieval modeli ile t\u00fcm dok\u00fcmanlar vekt\u00f6rle\u015ftirilip indeksleniyor. Chatbot sorgular\u0131 ayn\u0131 modelle encode edilip semantik e\u015fle\u015fme yap\u0131l\u0131yor. <\/li>\n
          4. Sonu\u00e7:<\/strong> Sistem ba\u011flama uygun dok\u00fcmanlar\u0131 \u00e7a\u011f\u0131r\u0131yor, yan\u0131t do\u011frulu\u011fu %40 art\u0131yor. <\/li>\n
          5. \u0130\u015f etkisi:<\/strong> Destek ekibinin yan\u0131t s\u00fcresi azal\u0131yor, m\u00fc\u015fteri memnuniyeti y\u00fckseliyor.<\/li>\n<\/ol>\n
            \n

            S\u0131k yap\u0131lan hatalar ve en iyi uygulamalar<\/strong><\/h3>\n
              \n
            • Hata:<\/strong> T\u00fcm verinin tek seferde encode edilmesi GPU bellek limitlerini a\u015far.
              \n\u00c7\u00f6z\u00fcm:<\/strong> Par\u00e7al\u0131 batch encode ve streaming index g\u00fcncellemeleri \u00f6nerilir. <\/li>\n
            • Hata:<\/strong> Farkl\u0131 embedding modellerinin kar\u0131\u015ft\u0131r\u0131lmas\u0131 tutars\u0131z arama sonu\u00e7lar\u0131 do\u011furur.
              \n\u00c7\u00f6z\u00fcm:<\/strong> Tek model politikas\u0131 veya domain temelli model birle\u015ftirme uygulanmal\u0131d\u0131r. <\/li>\n
            • En iyi uygulama:<\/strong> Periyodik yeniden e\u011fitim, semantik kaymay\u0131 \u00f6nler. Model g\u00fcncellemeleri ve index refresh d\u00f6ng\u00fcs\u00fc sistem kararl\u0131l\u0131\u011f\u0131n\u0131 garanti eder.<\/li>\n<\/ul>\n
              \n

              Sonu\u00e7<\/strong><\/h3>\n

              Dense retrieval, RAG ve vekt\u00f6r arama mimarilerinin merkezinde yer alan kritik bir bilgi getirme yakla\u015f\u0131m\u0131d\u0131r. Do\u011fru uyguland\u0131\u011f\u0131nda hem teknik performans\u0131 hem de i\u015f s\u00fcre\u00e7lerinin verimlili\u011fini belirgin \u015fekilde art\u0131r\u0131r. NeKu.AI\u2019nin bilgi taban\u0131 ve grounding yap\u0131lar\u0131nda bu y\u00f6ntemin kullan\u0131lmas\u0131, yapay zeka tabanl\u0131 karar sistemlerinin g\u00fcvenilirli\u011fini g\u00fc\u00e7lendiren somut bir pratiktir.<\/p>","protected":false},"excerpt":{"rendered":"

              Dense retrieval nedir Giri\u015f Dense retrieval, bilgi getirme (information retrieval) sistemlerinde metin ve dok\u00fcmanlar\u0131 vekt\u00f6r temelli olarak e\u015fle\u015ftiren modern bir y\u00f6ntemdir. \u00d6zellikle RAG (Retrieval-Augmented Generation) mimarilerinde, [\u2026]<\/span><\/p>\n","protected":false},"author":2,"featured_media":524,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_sitemap_exclude":false,"_sitemap_priority":"","_sitemap_frequency":"","footnotes":""},"categories":[],"tags":[],"class_list":["post-523","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry"],"yoast_head":"\nDense Retrieval ile RAG Sistemlerinde H\u0131zl\u0131 ve Do\u011fru Bilgi Getirme - NeKu.AI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/neku.ai\/en\/dense-retrieval-vektor-arama\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Dense Retrieval ile RAG Sistemlerinde H\u0131zl\u0131 ve Do\u011fru Bilgi Getirme - NeKu.AI\" \/>\n<meta property=\"og:description\" content=\"Dense retrieval nedir Giri\u015f Dense retrieval, bilgi getirme (information retrieval) sistemlerinde metin ve dok\u00fcmanlar\u0131 vekt\u00f6r temelli olarak e\u015fle\u015ftiren modern bir y\u00f6ntemdir. \u00d6zellikle RAG (Retrieval-Augmented Generation) mimarilerinde, [\u2026]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/neku.ai\/en\/dense-retrieval-vektor-arama\/\" \/>\n<meta property=\"og:site_name\" content=\"NeKu.AI\" \/>\n<meta property=\"article:published_time\" content=\"2025-12-14T17:00:48+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-12-14T17:01:11+00:00\" \/>\n<meta name=\"author\" content=\"Serkan \u00d6zcan\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Serkan \u00d6zcan\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/neku.ai\\\/dense-retrieval-vektor-arama\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/dense-retrieval-vektor-arama\\\/\"},\"author\":{\"name\":\"Serkan \u00d6zcan\",\"@id\":\"https:\\\/\\\/neku.ai\\\/#\\\/schema\\\/person\\\/cf640cfda3e16635fb740662d943e96b\"},\"headline\":\"Dense Retrieval ile RAG Sistemlerinde H\u0131zl\u0131 ve Do\u011fru Bilgi Getirme\",\"datePublished\":\"2025-12-14T17:00:48+00:00\",\"dateModified\":\"2025-12-14T17:01:11+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/dense-retrieval-vektor-arama\\\/\"},\"wordCount\":1023,\"publisher\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/dense-retrieval-vektor-arama\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/neku.ai\\\/wp-content\\\/uploads\\\/2025\\\/12\\\/cover-image-523.jpg\",\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/neku.ai\\\/dense-retrieval-vektor-arama\\\/\",\"url\":\"https:\\\/\\\/neku.ai\\\/dense-retrieval-vektor-arama\\\/\",\"name\":\"Dense Retrieval ile RAG Sistemlerinde H\u0131zl\u0131 ve Do\u011fru Bilgi Getirme - NeKu.AI\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/dense-retrieval-vektor-arama\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/dense-retrieval-vektor-arama\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/neku.ai\\\/wp-content\\\/uploads\\\/2025\\\/12\\\/cover-image-523.jpg\",\"datePublished\":\"2025-12-14T17:00:48+00:00\",\"dateModified\":\"2025-12-14T17:01:11+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/dense-retrieval-vektor-arama\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/neku.ai\\\/dense-retrieval-vektor-arama\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/neku.ai\\\/dense-retrieval-vektor-arama\\\/#primaryimage\",\"url\":\"https:\\\/\\\/neku.ai\\\/wp-content\\\/uploads\\\/2025\\\/12\\\/cover-image-523.jpg\",\"contentUrl\":\"https:\\\/\\\/neku.ai\\\/wp-content\\\/uploads\\\/2025\\\/12\\\/cover-image-523.jpg\",\"width\":1024,\"height\":1024},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/neku.ai\\\/dense-retrieval-vektor-arama\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Anasayfa\",\"item\":\"https:\\\/\\\/neku.ai\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Dense Retrieval ile RAG Sistemlerinde H\u0131zl\u0131 ve Do\u011fru Bilgi Getirme\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/neku.ai\\\/#website\",\"url\":\"https:\\\/\\\/neku.ai\\\/\",\"name\":\"NeKuAI\",\"description\":\"\u0130\u015fletmenizi daha "Ak\u0131ll\u0131" yap\u0131n\",\"publisher\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/neku.ai\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/neku.ai\\\/#organization\",\"name\":\"NeKuAI\",\"url\":\"https:\\\/\\\/neku.ai\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/neku.ai\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/neku.ai\\\/wp-content\\\/uploads\\\/2025\\\/02\\\/apple-icon-180x180-1.png\",\"contentUrl\":\"https:\\\/\\\/neku.ai\\\/wp-content\\\/uploads\\\/2025\\\/02\\\/apple-icon-180x180-1.png\",\"width\":180,\"height\":180,\"caption\":\"NeKuAI\"},\"image\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/#\\\/schema\\\/logo\\\/image\\\/\"}},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/neku.ai\\\/#\\\/schema\\\/person\\\/cf640cfda3e16635fb740662d943e96b\",\"name\":\"Serkan \u00d6zcan\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/neku.ai\\\/wp-content\\\/plugins\\\/swiss-toolkit-for-wp\\\/\\\/admin\\\/img\\\/default-avatar.png\",\"url\":\"https:\\\/\\\/neku.ai\\\/wp-content\\\/plugins\\\/swiss-toolkit-for-wp\\\/\\\/admin\\\/img\\\/default-avatar.png\",\"contentUrl\":\"https:\\\/\\\/neku.ai\\\/wp-content\\\/plugins\\\/swiss-toolkit-for-wp\\\/\\\/admin\\\/img\\\/default-avatar.png\",\"caption\":\"Serkan \u00d6zcan\"},\"url\":\"https:\\\/\\\/neku.ai\\\/en\\\/author\\\/serkanozcan\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Dense Retrieval ile RAG Sistemlerinde H\u0131zl\u0131 ve Do\u011fru Bilgi Getirme - NeKu.AI","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/neku.ai\/en\/dense-retrieval-vektor-arama\/","og_locale":"en_US","og_type":"article","og_title":"Dense Retrieval ile RAG Sistemlerinde H\u0131zl\u0131 ve Do\u011fru Bilgi Getirme - NeKu.AI","og_description":"Dense retrieval nedir Giri\u015f Dense retrieval, bilgi getirme (information retrieval) sistemlerinde metin ve dok\u00fcmanlar\u0131 vekt\u00f6r temelli olarak e\u015fle\u015ftiren modern bir y\u00f6ntemdir. \u00d6zellikle RAG (Retrieval-Augmented Generation) mimarilerinde, [\u2026]","og_url":"https:\/\/neku.ai\/en\/dense-retrieval-vektor-arama\/","og_site_name":"NeKu.AI","article_published_time":"2025-12-14T17:00:48+00:00","article_modified_time":"2025-12-14T17:01:11+00:00","author":"Serkan \u00d6zcan","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Serkan \u00d6zcan","Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/neku.ai\/dense-retrieval-vektor-arama\/#article","isPartOf":{"@id":"https:\/\/neku.ai\/dense-retrieval-vektor-arama\/"},"author":{"name":"Serkan \u00d6zcan","@id":"https:\/\/neku.ai\/#\/schema\/person\/cf640cfda3e16635fb740662d943e96b"},"headline":"Dense Retrieval ile RAG Sistemlerinde H\u0131zl\u0131 ve Do\u011fru Bilgi Getirme","datePublished":"2025-12-14T17:00:48+00:00","dateModified":"2025-12-14T17:01:11+00:00","mainEntityOfPage":{"@id":"https:\/\/neku.ai\/dense-retrieval-vektor-arama\/"},"wordCount":1023,"publisher":{"@id":"https:\/\/neku.ai\/#organization"},"image":{"@id":"https:\/\/neku.ai\/dense-retrieval-vektor-arama\/#primaryimage"},"thumbnailUrl":"https:\/\/neku.ai\/wp-content\/uploads\/2025\/12\/cover-image-523.jpg","inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/neku.ai\/dense-retrieval-vektor-arama\/","url":"https:\/\/neku.ai\/dense-retrieval-vektor-arama\/","name":"Dense Retrieval ile RAG Sistemlerinde H\u0131zl\u0131 ve Do\u011fru Bilgi Getirme - NeKu.AI","isPartOf":{"@id":"https:\/\/neku.ai\/#website"},"primaryImageOfPage":{"@id":"https:\/\/neku.ai\/dense-retrieval-vektor-arama\/#primaryimage"},"image":{"@id":"https:\/\/neku.ai\/dense-retrieval-vektor-arama\/#primaryimage"},"thumbnailUrl":"https:\/\/neku.ai\/wp-content\/uploads\/2025\/12\/cover-image-523.jpg","datePublished":"2025-12-14T17:00:48+00:00","dateModified":"2025-12-14T17:01:11+00:00","breadcrumb":{"@id":"https:\/\/neku.ai\/dense-retrieval-vektor-arama\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/neku.ai\/dense-retrieval-vektor-arama\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/neku.ai\/dense-retrieval-vektor-arama\/#primaryimage","url":"https:\/\/neku.ai\/wp-content\/uploads\/2025\/12\/cover-image-523.jpg","contentUrl":"https:\/\/neku.ai\/wp-content\/uploads\/2025\/12\/cover-image-523.jpg","width":1024,"height":1024},{"@type":"BreadcrumbList","@id":"https:\/\/neku.ai\/dense-retrieval-vektor-arama\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Anasayfa","item":"https:\/\/neku.ai\/"},{"@type":"ListItem","position":2,"name":"Dense Retrieval ile RAG Sistemlerinde H\u0131zl\u0131 ve Do\u011fru Bilgi Getirme"}]},{"@type":"WebSite","@id":"https:\/\/neku.ai\/#website","url":"https:\/\/neku.ai\/","name":"NeKuAI","description":"\u0130\u015fletmenizi daha "Ak\u0131ll\u0131" yap\u0131n","publisher":{"@id":"https:\/\/neku.ai\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/neku.ai\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/neku.ai\/#organization","name":"NeKuAI","url":"https:\/\/neku.ai\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/neku.ai\/#\/schema\/logo\/image\/","url":"https:\/\/neku.ai\/wp-content\/uploads\/2025\/02\/apple-icon-180x180-1.png","contentUrl":"https:\/\/neku.ai\/wp-content\/uploads\/2025\/02\/apple-icon-180x180-1.png","width":180,"height":180,"caption":"NeKuAI"},"image":{"@id":"https:\/\/neku.ai\/#\/schema\/logo\/image\/"}},{"@type":"Person","@id":"https:\/\/neku.ai\/#\/schema\/person\/cf640cfda3e16635fb740662d943e96b","name":"Serkan \u00d6zcan","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/neku.ai\/wp-content\/plugins\/swiss-toolkit-for-wp\/\/admin\/img\/default-avatar.png","url":"https:\/\/neku.ai\/wp-content\/plugins\/swiss-toolkit-for-wp\/\/admin\/img\/default-avatar.png","contentUrl":"https:\/\/neku.ai\/wp-content\/plugins\/swiss-toolkit-for-wp\/\/admin\/img\/default-avatar.png","caption":"Serkan \u00d6zcan"},"url":"https:\/\/neku.ai\/en\/author\/serkanozcan\/"}]}},"_links":{"self":[{"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/posts\/523","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/comments?post=523"}],"version-history":[{"count":0,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/posts\/523\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/media\/524"}],"wp:attachment":[{"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/media?parent=523"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/categories?post=523"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/tags?post=523"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}