{"id":538,"date":"2025-12-16T08:01:06","date_gmt":"2025-12-16T05:01:06","guid":{"rendered":"https:\/\/neku.ai\/rag-sistemlerinde-indexing\/"},"modified":"2025-12-16T08:01:50","modified_gmt":"2025-12-16T05:01:50","slug":"rag-sistemlerinde-indexing","status":"publish","type":"post","link":"https:\/\/neku.ai\/en\/rag-sistemlerinde-indexing\/","title":{"rendered":"RAG sistemlerinde do\u011fru indexing ile veri eri\u015fimini h\u0131zland\u0131r\u0131n"},"content":{"rendered":"

Indexing nedir<\/strong><\/h1>\n
\n

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

Indexing, yani veri endeksleme, bilgi getirme (information retrieval) sistemlerinin temelini olu\u015fturan bir s\u00fcre\u00e7tir. B\u00fcy\u00fck \u00f6l\u00e7ekli yapay zeka modelleri ve Retrieval-Augmented Generation (RAG)<\/strong> mimarileri, veriye h\u0131zl\u0131 ve do\u011fru eri\u015fim i\u00e7in g\u00fc\u00e7l\u00fc bir indexing katman\u0131na ihtiya\u00e7 duyar. Bu s\u00fcre\u00e7, vekt\u00f6r arama ve dok\u00fcman i\u015fleme ad\u0131mlar\u0131nda modelin performans\u0131n\u0131 do\u011frudan etkiler.<\/p>\n

\n

Indexing nedir tan\u0131m\u0131<\/strong><\/h3>\n

Indexing, veri veya dok\u00fcmanlar\u0131n belirli bir yap\u0131ya d\u00f6n\u00fc\u015ft\u00fcr\u00fclerek h\u0131zl\u0131 eri\u015fim i\u00e7in organize edilmesi i\u015flemidir. RAG sistemlerinde indexing, metin, g\u00f6rsel ya da yap\u0131land\u0131r\u0131lmam\u0131\u015f verilerin vekt\u00f6r temsillerine d\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcp aranabilir hale getirilmesini sa\u011flar. Bu yap\u0131, bilgi getirme algoritmalar\u0131n\u0131n belirli bir sorguya en uygun i\u00e7eri\u011fi milisaniyeler i\u00e7inde bulmas\u0131na yard\u0131mc\u0131 olur.<\/p>\n

\n

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

Indexing s\u00fcreci, gelen ham verinin analizi, d\u00f6n\u00fc\u015ft\u00fcr\u00fclmesi ve sorgu an\u0131nda y\u00fcksek do\u011frulukla geri getirilecek \u015fekilde yap\u0131land\u0131r\u0131lmas\u0131yla i\u015fler. RAG veya benzeri sistemlerde bu, embedding modelleriyle veri temsili olu\u015fturma, vekt\u00f6r veritaban\u0131na yazma ve uygun arama stratejilerini uygulama ad\u0131mlar\u0131n\u0131 i\u00e7erir.<\/p>\n

\n

Temel parametreler ve ayarlar<\/strong><\/h3>\n
    \n
  • Vekt\u00f6r boyutu (embedding dimension):<\/strong> Modelin \u00e7\u0131k\u0131\u015f vekt\u00f6rlerinin uzunlu\u011funu belirler. Genellikle 384 ila 1536 boyut aras\u0131nda de\u011fi\u015fir. <\/li>\n
  • Benzerlik metri\u011fi:<\/strong> Kosin\u00fcs benzerli\u011fi, \u00d6klid uzakl\u0131\u011f\u0131 ya da dot product y\u00f6ntemleri kullan\u0131l\u0131r. <\/li>\n
  • Index tipi:<\/strong> HNSW, FAISS, Annoy gibi yap\u0131land\u0131rmalar, performans ve bellek optimizasyonuna g\u00f6re se\u00e7ilir. <\/li>\n
  • Batch b\u00fcy\u00fckl\u00fc\u011f\u00fc ve segmentleme:<\/strong> B\u00fcy\u00fck veri setlerinde paralel indexing yap\u0131l\u0131rken sorgu performans\u0131n\u0131 belirleyen kritik fakt\u00f6rlerdir.<\/li>\n<\/ul>\n
    \n

    S\u0131k yap\u0131lan hatalar ve ka\u00e7\u0131nma y\u00f6ntemleri<\/strong><\/h3>\n
      \n
    • Normalize edilmemi\u015f embedding\u2019ler:<\/strong> Vekt\u00f6rlerin normalize edilmemesi, benzerlik hesaplar\u0131nda yanl\u0131\u015f sonu\u00e7lara yol a\u00e7abilir. <\/li>\n
    • Yanl\u0131\u015f metrik se\u00e7imi:<\/strong> Verinin do\u011fas\u0131na uygun olmayan benzerlik metri\u011fi, sorgu ba\u015far\u0131s\u0131n\u0131 d\u00fc\u015f\u00fcr\u00fcr. <\/li>\n
    • Eksik dok\u00fcman temsili:<\/strong> \u00d6zellikle uzun metinlerde par\u00e7alara ay\u0131rma (chunking) stratejisi yanl\u0131\u015f se\u00e7ilirse bilgi kayb\u0131 olur.<\/li>\n<\/ul>\n

      Bu hatalar, do\u011fru vekt\u00f6r arama yap\u0131land\u0131rmas\u0131 ve test senaryolar\u0131yla erken a\u015famada \u00f6nlenebilir.<\/p>\n

      \n

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

      Bir RAG altyap\u0131s\u0131nda kullan\u0131c\u0131 sorgusu geldi\u011finde:<\/p>\n

        \n
      1. Sorgu embedding temsiline d\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcr. <\/li>\n
      2. Index i\u00e7indeki en benzer vekt\u00f6rler bulunur. <\/li>\n
      3. Elde edilen dok\u00fcmanlar modele ba\u011flamsal giri\u015f olarak verilir.
        \nBu s\u00fcre\u00e7, \u00fcretken modelin daha do\u011fru ve ba\u011flama uygun yan\u0131tlar \u00fcretmesini sa\u011flar.<\/li>\n<\/ol>\n
        \n

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

        Indexing motoru, veri al\u0131m\u0131ndan arama i\u015flemlerine kadar birden fazla a\u015famada \u00e7al\u0131\u015f\u0131r. \u0130lk a\u015famada metin veya dok\u00fcman embedding modelleriyle y\u00fcksek boyutlu vekt\u00f6rlere \u00e7evrilir. Ard\u0131ndan bu vekt\u00f6rler FAISS, Milvus veya Weaviate gibi veritabanlar\u0131nda indekslenir. <\/p>\n

        RAG mimarilerinde, sorgu geldi\u011finde sistem embedding uzay\u0131nda en yak\u0131n vekt\u00f6rleri bulur. Bu i\u015flem, approximate nearest neighbor (ANN) algoritmalar\u0131yla optimize edilir. \u0130yi yap\u0131land\u0131r\u0131lm\u0131\u015f bir indexing mekanizmas\u0131, bilgi getirme do\u011frulu\u011funu %20\u201340 oran\u0131nda art\u0131rabilir ve modelin grounding kapasitesini g\u00fc\u00e7lendirir.<\/p>\n

        \n

        \u0130\u015fletmeler i\u00e7in neden kritiktir<\/strong><\/h3>\n
          \n
        • Performans:<\/strong> Milisaniye d\u00fczeyinde bilgi eri\u015fimi sa\u011flar. <\/li>\n
        • G\u00fcvenilirlik:<\/strong> Ayn\u0131 veri \u00fczerinde tutarl\u0131 sorgu sonu\u00e7lar\u0131 \u00fcretir. <\/li>\n
        • Maliyet:<\/strong> Gereksiz model \u00e7a\u011fr\u0131lar\u0131n\u0131 azaltarak i\u015flem maliyetini d\u00fc\u015f\u00fcr\u00fcr. <\/li>\n
        • \u00d6l\u00e7ekleme:<\/strong> Artan veri hacmine paralel olarak daha kolay geni\u015fletilir. <\/li>\n
        • Otomasyon:<\/strong> Veri boru hatlar\u0131nda s\u00fcrekli g\u00fcncelleme ve yeniden indeksleme yap\u0131labilir. <\/li>\n
        • Karar alma:<\/strong> G\u00fcncel bilginin an\u0131nda eri\u015filebilir olmas\u0131 analitik s\u00fcre\u00e7leri h\u0131zland\u0131r\u0131r. <\/li>\n
        • Operasyonel verimlilik:<\/strong> \u00c7al\u0131\u015fan veya sistem sorgular\u0131na daha do\u011fru yan\u0131t verilir.<\/li>\n<\/ul>\n
          \n

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

          NeKu.AI, bilgi tabanlar\u0131n\u0131 destekleyen grounding mimarisi<\/strong> i\u00e7inde geli\u015fmi\u015f bir indexing katman\u0131 kullan\u0131r. Dok\u00fcmanlar, yap\u0131land\u0131r\u0131lm\u0131\u015f embedding pipeline\u2019lar\u0131 arac\u0131l\u0131\u011f\u0131yla i\u015flenir ve vekt\u00f6r tabanl\u0131 arama motoruna kaydedilir. Bu sayede NeKu.AI, kullan\u0131c\u0131 sorgular\u0131n\u0131 sadece dil modeline de\u011fil, kurumsal bilgi havuzuna da dayand\u0131rabilir. <\/p>\n

          Indexing s\u00fcreci otomatik olarak versiyonlanabilir, bu da sistemin g\u00fcncel verilere dayanarak karar \u00fcretmesini garanti eder.<\/p>\n

          \n

          AI geli\u015ftiricileri, veri m\u00fchendisleri i\u00e7in ger\u00e7ek bir senaryo<\/strong><\/h3>\n
            \n
          1. Sorun:<\/strong> Bir kurumsal destek botu, mevcut bilgi taban\u0131ndaki dok\u00fcmanlar\u0131n yaln\u0131zca bir k\u0131sm\u0131n\u0131 do\u011fru getiriyor. <\/li>\n
          2. Ba\u011flam:<\/strong> Dok\u00fcman i\u015fleme s\u00fcrecinde embedding modeline veriler tutars\u0131z bi\u00e7imde aktar\u0131lm\u0131\u015f. <\/li>\n
          3. Kavram\u0131n uygulanmas\u0131:<\/strong> T\u00fcm dok\u00fcmanlar yeniden indexing i\u015flemine al\u0131n\u0131r, embedding\u2019ler normalize edilir, FAISS temelli vekt\u00f6r arama yap\u0131land\u0131r\u0131l\u0131r. <\/li>\n
          4. Sonu\u00e7:<\/strong> Bilgi getirme ba\u015far\u0131m\u0131 %35 artar, modelin yan\u0131tlar\u0131 daha tutarl\u0131 hale gelir. <\/li>\n
          5. \u0130\u015f etkisi:<\/strong> Destek s\u00fcre\u00e7leri otomatikle\u015fir ve kullan\u0131c\u0131 deneyimi belirgin \u015fekilde iyile\u015fir.<\/li>\n<\/ol>\n
            \n

            S\u0131k yap\u0131lan hatalar ve en iyi uygulamalar<\/strong><\/h3>\n

            Yayg\u0131n hatalar<\/strong> <\/p>\n

              \n
            • Indexing s\u0131ras\u0131nda veri t\u00fcrlerinin kar\u0131\u015ft\u0131r\u0131lmas\u0131 <\/li>\n
            • Embedding g\u00fcncellemelerinin versiyonlanmamas\u0131 <\/li>\n
            • Arama e\u015fi\u011fi (similarity threshold) de\u011ferinin k\u00f6rlemesine se\u00e7ilmesi <\/li>\n<\/ul>\n

              En iyi uygulamalar<\/strong> <\/p>\n

                \n
              • Embedding modellerinin s\u00fcr\u00fcm kontrol\u00fcyle kullan\u0131lmas\u0131 <\/li>\n
              • Art\u0131ml\u0131 (incremental) indexing deste\u011finin etkinle\u015ftirilmesi <\/li>\n
              • Vekt\u00f6r veritaban\u0131n\u0131n sorgu y\u00fck\u00fcne g\u00f6re optimize edilmesi <\/li>\n
              • Dok\u00fcman i\u015fleme s\u00fcrecinde meta verilerin korunmas\u0131 <\/li>\n<\/ul>\n
                \n

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

                Indexing, RAG ve bilgi getirme mimarilerinin g\u00f6r\u00fcnmeyen ama en kritik katman\u0131d\u0131r. Do\u011fru yap\u0131land\u0131r\u0131lm\u0131\u015f bir index, yapay zekaya dayal\u0131 sistemlerde hem teknik hem de operasyonel verimlili\u011fi belirler. <\/p>\n

                NeKu.AI\u2019nin bilgi taban\u0131 altyap\u0131s\u0131nda oldu\u011fu gibi, g\u00fc\u00e7l\u00fc bir indexing stratejisi yaln\u0131zca h\u0131zl\u0131 veri eri\u015fimi de\u011fil, ayn\u0131 zamanda g\u00fcvenilir ve a\u00e7\u0131klanabilir yapay zeka \u00e7\u0131kt\u0131lar\u0131 i\u00e7in de temel olu\u015fturur.<\/p>","protected":false},"excerpt":{"rendered":"

                Indexing nedir Giri\u015f Indexing, yani veri endeksleme, bilgi getirme (information retrieval) sistemlerinin temelini olu\u015fturan bir s\u00fcre\u00e7tir. B\u00fcy\u00fck \u00f6l\u00e7ekli yapay zeka modelleri ve Retrieval-Augmented Generation (RAG) mimarileri, [\u2026]<\/span><\/p>\n","protected":false},"author":2,"featured_media":539,"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-538","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry"],"yoast_head":"\nRAG sistemlerinde do\u011fru indexing ile veri eri\u015fimini h\u0131zland\u0131r\u0131n - 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\/rag-sistemlerinde-indexing\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"RAG sistemlerinde do\u011fru indexing ile veri eri\u015fimini h\u0131zland\u0131r\u0131n - NeKu.AI\" \/>\n<meta property=\"og:description\" content=\"Indexing nedir Giri\u015f Indexing, yani veri endeksleme, bilgi getirme (information retrieval) sistemlerinin temelini olu\u015fturan bir s\u00fcre\u00e7tir. B\u00fcy\u00fck \u00f6l\u00e7ekli yapay zeka modelleri ve Retrieval-Augmented Generation (RAG) mimarileri, [\u2026]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/neku.ai\/en\/rag-sistemlerinde-indexing\/\" \/>\n<meta property=\"og:site_name\" content=\"NeKu.AI\" \/>\n<meta property=\"article:published_time\" content=\"2025-12-16T05:01:06+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-12-16T05:01:50+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\\\/rag-sistemlerinde-indexing\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/rag-sistemlerinde-indexing\\\/\"},\"author\":{\"name\":\"Serkan \u00d6zcan\",\"@id\":\"https:\\\/\\\/neku.ai\\\/#\\\/schema\\\/person\\\/cf640cfda3e16635fb740662d943e96b\"},\"headline\":\"RAG sistemlerinde do\u011fru indexing ile veri eri\u015fimini h\u0131zland\u0131r\u0131n\",\"datePublished\":\"2025-12-16T05:01:06+00:00\",\"dateModified\":\"2025-12-16T05:01:50+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/rag-sistemlerinde-indexing\\\/\"},\"wordCount\":992,\"publisher\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/rag-sistemlerinde-indexing\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/neku.ai\\\/wp-content\\\/uploads\\\/2025\\\/12\\\/cover-image-538.jpg\",\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/neku.ai\\\/rag-sistemlerinde-indexing\\\/\",\"url\":\"https:\\\/\\\/neku.ai\\\/rag-sistemlerinde-indexing\\\/\",\"name\":\"RAG sistemlerinde do\u011fru indexing ile veri eri\u015fimini h\u0131zland\u0131r\u0131n - NeKu.AI\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/rag-sistemlerinde-indexing\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/rag-sistemlerinde-indexing\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/neku.ai\\\/wp-content\\\/uploads\\\/2025\\\/12\\\/cover-image-538.jpg\",\"datePublished\":\"2025-12-16T05:01:06+00:00\",\"dateModified\":\"2025-12-16T05:01:50+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/rag-sistemlerinde-indexing\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/neku.ai\\\/rag-sistemlerinde-indexing\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/neku.ai\\\/rag-sistemlerinde-indexing\\\/#primaryimage\",\"url\":\"https:\\\/\\\/neku.ai\\\/wp-content\\\/uploads\\\/2025\\\/12\\\/cover-image-538.jpg\",\"contentUrl\":\"https:\\\/\\\/neku.ai\\\/wp-content\\\/uploads\\\/2025\\\/12\\\/cover-image-538.jpg\",\"width\":1024,\"height\":1024},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/neku.ai\\\/rag-sistemlerinde-indexing\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Anasayfa\",\"item\":\"https:\\\/\\\/neku.ai\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"RAG sistemlerinde do\u011fru indexing ile veri eri\u015fimini h\u0131zland\u0131r\u0131n\"}]},{\"@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":"RAG sistemlerinde do\u011fru indexing ile veri eri\u015fimini h\u0131zland\u0131r\u0131n - 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\/rag-sistemlerinde-indexing\/","og_locale":"en_US","og_type":"article","og_title":"RAG sistemlerinde do\u011fru indexing ile veri eri\u015fimini h\u0131zland\u0131r\u0131n - NeKu.AI","og_description":"Indexing nedir Giri\u015f Indexing, yani veri endeksleme, bilgi getirme (information retrieval) sistemlerinin temelini olu\u015fturan bir s\u00fcre\u00e7tir. B\u00fcy\u00fck \u00f6l\u00e7ekli yapay zeka modelleri ve Retrieval-Augmented Generation (RAG) mimarileri, [\u2026]","og_url":"https:\/\/neku.ai\/en\/rag-sistemlerinde-indexing\/","og_site_name":"NeKu.AI","article_published_time":"2025-12-16T05:01:06+00:00","article_modified_time":"2025-12-16T05:01:50+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\/rag-sistemlerinde-indexing\/#article","isPartOf":{"@id":"https:\/\/neku.ai\/rag-sistemlerinde-indexing\/"},"author":{"name":"Serkan \u00d6zcan","@id":"https:\/\/neku.ai\/#\/schema\/person\/cf640cfda3e16635fb740662d943e96b"},"headline":"RAG sistemlerinde do\u011fru indexing ile veri eri\u015fimini h\u0131zland\u0131r\u0131n","datePublished":"2025-12-16T05:01:06+00:00","dateModified":"2025-12-16T05:01:50+00:00","mainEntityOfPage":{"@id":"https:\/\/neku.ai\/rag-sistemlerinde-indexing\/"},"wordCount":992,"publisher":{"@id":"https:\/\/neku.ai\/#organization"},"image":{"@id":"https:\/\/neku.ai\/rag-sistemlerinde-indexing\/#primaryimage"},"thumbnailUrl":"https:\/\/neku.ai\/wp-content\/uploads\/2025\/12\/cover-image-538.jpg","inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/neku.ai\/rag-sistemlerinde-indexing\/","url":"https:\/\/neku.ai\/rag-sistemlerinde-indexing\/","name":"RAG sistemlerinde do\u011fru indexing ile veri eri\u015fimini h\u0131zland\u0131r\u0131n - NeKu.AI","isPartOf":{"@id":"https:\/\/neku.ai\/#website"},"primaryImageOfPage":{"@id":"https:\/\/neku.ai\/rag-sistemlerinde-indexing\/#primaryimage"},"image":{"@id":"https:\/\/neku.ai\/rag-sistemlerinde-indexing\/#primaryimage"},"thumbnailUrl":"https:\/\/neku.ai\/wp-content\/uploads\/2025\/12\/cover-image-538.jpg","datePublished":"2025-12-16T05:01:06+00:00","dateModified":"2025-12-16T05:01:50+00:00","breadcrumb":{"@id":"https:\/\/neku.ai\/rag-sistemlerinde-indexing\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/neku.ai\/rag-sistemlerinde-indexing\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/neku.ai\/rag-sistemlerinde-indexing\/#primaryimage","url":"https:\/\/neku.ai\/wp-content\/uploads\/2025\/12\/cover-image-538.jpg","contentUrl":"https:\/\/neku.ai\/wp-content\/uploads\/2025\/12\/cover-image-538.jpg","width":1024,"height":1024},{"@type":"BreadcrumbList","@id":"https:\/\/neku.ai\/rag-sistemlerinde-indexing\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Anasayfa","item":"https:\/\/neku.ai\/"},{"@type":"ListItem","position":2,"name":"RAG sistemlerinde do\u011fru indexing ile veri eri\u015fimini h\u0131zland\u0131r\u0131n"}]},{"@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\/538","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=538"}],"version-history":[{"count":0,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/posts\/538\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/media\/539"}],"wp:attachment":[{"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/media?parent=538"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/categories?post=538"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/tags?post=538"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}