{"id":514,"date":"2025-12-13T20:00:57","date_gmt":"2025-12-13T17:00:57","guid":{"rendered":"https:\/\/neku.ai\/text-splitting-rag-sistemleri\/"},"modified":"2025-12-13T20:01:21","modified_gmt":"2025-12-13T17:01:21","slug":"text-splitting-rag-sistemleri","status":"publish","type":"post","link":"https:\/\/neku.ai\/en\/text-splitting-rag-sistemleri\/","title":{"rendered":"RAG sistemlerinde text splitting ile daha dogru bilgi erisimi"},"content":{"rendered":"

Text splitting nedir<\/strong><\/h1>\n
\n

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

Text splitting, b\u00fcy\u00fck metin verilerini daha k\u00fc\u00e7\u00fck ve anlaml\u0131 par\u00e7alara ay\u0131rma i\u015flemidir. RAG (Retrieval-Augmented Generation) ve bilgi getirme mimarilerinde bu i\u015flem, modelin dok\u00fcmanlar\u0131 do\u011fru \u015fekilde anlamas\u0131 ve verimli arama yapmas\u0131 i\u00e7in kritik bir ad\u0131md\u0131r. Bu yakla\u015f\u0131m, \u00f6zellikle vekt\u00f6r arama ve dok\u00fcman i\u015fleme s\u00fcre\u00e7lerinde do\u011fruluk ve h\u0131z a\u00e7\u0131s\u0131ndan belirleyicidir.<\/p>\n

\n

Text splitting nedir tan\u0131m\u0131<\/strong><\/h3>\n

Text splitting, bir metnin belirli kriterlere g\u00f6re segmentlere b\u00f6l\u00fcnmesidir. Bu segmentler genellikle sabit uzunlukta ya da i\u00e7erik temelli olarak olu\u015fturulur. Ama\u00e7, metni dil modelleri veya bilgi getirme sistemlerinin i\u015fleyebilece\u011fi formatta sunmakt\u0131r. \u00d6zellikle b\u00fcy\u00fck dil modellerine (LLM) dayal\u0131 sistemlerde, token s\u0131n\u0131rlar\u0131n\u0131n a\u015f\u0131lmamas\u0131 ve ba\u011flam b\u00fct\u00fcnl\u00fc\u011f\u00fcn\u00fcn korunmas\u0131 i\u00e7in kullan\u0131l\u0131r.<\/p>\n

\n

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

Text splitting s\u00fcreci, bir dok\u00fcman\u0131 belirli par\u00e7alara ay\u0131rarak bu par\u00e7alar\u0131 indeksleme, vekt\u00f6rle\u015ftirme ve sorgulama a\u015famalar\u0131nda kullan\u0131labilir hale getirir. \u00c7al\u0131\u015fma mant\u0131\u011f\u0131 temelde \u00fc\u00e7 bile\u015fenden olu\u015fur: b\u00f6lme stratejisi, par\u00e7alar\u0131n boyutu ve \u00f6rt\u00fc\u015fme (overlap) oran\u0131. <\/p>\n

Temel parametreler ve ayarlar<\/strong><\/h4>\n
    \n
  • Chunk size (par\u00e7a boyutu):<\/strong> Genellikle 500\u20131000 token aras\u0131 tutulur. Daha k\u00fc\u00e7\u00fck de\u011ferler ba\u011flam\u0131 zay\u0131flat\u0131rken, b\u00fcy\u00fck de\u011ferler hesaplama maliyetini art\u0131r\u0131r. <\/li>\n
  • Overlap (\u00f6rt\u00fc\u015fme oran\u0131):<\/strong> \u0130ki par\u00e7a aras\u0131ndaki ortak metin miktar\u0131d\u0131r. Ba\u011flam kayb\u0131n\u0131 \u00f6nler. Tipik de\u011fer %10-15\u2019tir. <\/li>\n
  • Splitting strategy:<\/strong> C\u00fcmle bazl\u0131, paragraf bazl\u0131 ya da semantik yo\u011funluk temelli b\u00f6lme algoritmalar\u0131 kullan\u0131labilir. <\/li>\n<\/ul>\n

    S\u0131k yap\u0131lan hatalar ve ka\u00e7\u0131nma y\u00f6ntemleri<\/strong><\/h4>\n
      \n
    • A\u015f\u0131r\u0131 k\u00fc\u00e7\u00fck par\u00e7alara b\u00f6lme:<\/strong> Model ba\u011flam\u0131 kaybeder. <\/li>\n
    • Yetersiz overlap:<\/strong> Sonu\u00e7ta ba\u011flam b\u00fct\u00fcnl\u00fc\u011f\u00fc bozulur. <\/li>\n
    • Dil veya bi\u00e7im fark\u0131 g\u00f6zetmemek:<\/strong> \u00d6zellikle \u00e7ok dilli dok\u00fcmanlarda do\u011fru b\u00f6lme kurallar\u0131 uygulanmal\u0131d\u0131r. <\/li>\n<\/ul>\n

      Ger\u00e7ek sistemlerde uygulama \u00f6rnekleri<\/strong><\/h4>\n

      Bir RAG sisteminde PDF dok\u00fcmanlar\u0131, \u00f6nce text splitting s\u00fcrecinden ge\u00e7irilir. Her par\u00e7a metin vekt\u00f6rle\u015ftirilir ve vekt\u00f6r arama i\u00e7in bir embedding veritaban\u0131na kaydedilir. Sorgu geldi\u011finde en alakal\u0131 metin par\u00e7alar\u0131 geri getirilir ve model bu par\u00e7alar\u0131 kullanarak ba\u011flama dayal\u0131 yan\u0131t \u00fcretir.<\/p>\n

      \n

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

      Text splitting, bilgi getirme zincirinin ilk halkas\u0131d\u0131r. Sistem tasar\u0131m\u0131nda \u00fc\u00e7 temel ad\u0131m bulunur:<\/p>\n

        \n
      1. Dok\u00fcman \u00f6n i\u015fleme:<\/strong> PDF, e-posta, SAP raporu veya veri taban\u0131 kay\u0131tlar\u0131 gibi kaynaklardan metin \u00e7\u0131kar\u0131l\u0131r. <\/li>\n
      2. Splitting:<\/strong> Metin i\u00e7erik bazl\u0131 analiz edilerek uygun boyutlarda par\u00e7alara ayr\u0131l\u0131r. <\/li>\n
      3. Vekt\u00f6rle\u015ftirme:<\/strong> Her par\u00e7a embedding modeline g\u00f6nderilerek vekt\u00f6r temsiline d\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcr. <\/li>\n<\/ol>\n

        Bu zincir, vekt\u00f6r arama performans\u0131n\u0131 do\u011frudan etkiler. Par\u00e7a boyutlar\u0131n\u0131n homojen olmas\u0131, sorgu e\u015fle\u015fme do\u011frulu\u011funu art\u0131r\u0131r. NeKu.AI gibi geli\u015fmi\u015f bilgi taban\u0131 sistemlerinde splitting i\u015flemi, grounding mimarisiyle birlikte \u00e7al\u0131\u015farak do\u011fru ba\u011flam\u0131n getirilmesini sa\u011flar.<\/p>\n

        \n

        \u0130\u015fletmeler i\u00e7in neden kritiktir<\/strong><\/h3>\n
          \n
        • Performans:<\/strong> Arama ve sorgu yan\u0131t s\u00fcreleri k\u0131sal\u0131r. <\/li>\n
        • G\u00fcvenilirlik:<\/strong> Bilgi kayb\u0131 \u00f6nlenir, ba\u011flam tutarl\u0131l\u0131\u011f\u0131 korunur. <\/li>\n
        • Maliyet:<\/strong> Par\u00e7a boyutlar\u0131 optimize edilirse i\u015flem maliyetleri azal\u0131r. <\/li>\n
        • \u00d6l\u00e7ekleme:<\/strong> B\u00fcy\u00fck dok\u00fcman setleri y\u00f6netilebilir hale gelir. <\/li>\n
        • Otomasyon:<\/strong> S\u00fcre\u00e7 otomatik \u00e7al\u0131\u015farak veri ak\u0131\u015flar\u0131n\u0131 h\u0131zland\u0131r\u0131r. <\/li>\n
        • Karar alma:<\/strong> Do\u011fru bilgiye h\u0131zl\u0131 eri\u015fim sa\u011flan\u0131r. <\/li>\n
        • Operasyonel verimlilik:<\/strong> Bilgi tabanlar\u0131 g\u00fcncel ve eri\u015filebilir kal\u0131r. <\/li>\n<\/ul>\n
          \n

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

          NeKu.AI, bilgi taban\u0131 dok\u00fcmanlar\u0131n\u0131 i\u015flerken metinleri otomatik olarak uygun par\u00e7alara b\u00f6ler. Bu i\u015flem, grounding mimarisi sayesinde do\u011fru ba\u011flam e\u015fle\u015ftirmesi yap\u0131lmas\u0131n\u0131 sa\u011flar. Text splitting ad\u0131mlar\u0131, RAG zincirinin ilk katman\u0131nda \u00e7al\u0131\u015f\u0131r ve n8n benzeri orkestrasyon s\u00fcre\u00e7leriyle uyumlu \u015fekilde y\u00fcr\u00fct\u00fcl\u00fcr. B\u00f6ylece SAP ve di\u011fer kurumsal sistemlerden al\u0131nan veriler tutarl\u0131 bi\u00e7imde sorgulanabilir hale gelir.<\/p>\n

          \n

          AI geli\u015ftiricileri, veri m\u00fchendisleri i\u00e7in ger\u00e7ek bir senaryo<\/strong><\/h3>\n
            \n
          1. Sorun:<\/strong> Bir finans kurumunda m\u00fc\u015fteri s\u00f6zle\u015fmeleri y\u00fczlerce sayfadan olu\u015fuyor ve RAG sisteminde sorgular uzun yan\u0131tlar \u00fcretiyor. <\/li>\n
          2. Ba\u011flam:<\/strong> Dok\u00fcmanlar PDF format\u0131nda ve farkl\u0131 dillerde. <\/li>\n
          3. Kavram\u0131n uygulanmas\u0131:<\/strong> Text splitting uygulanarak her dok\u00fcman 800 token\u2019l\u0131k par\u00e7alar haline getiriliyor. %15 overlap ekleniyor. Par\u00e7alar embedding modeliyle vekt\u00f6rle\u015ftiriliyor. <\/li>\n
          4. Sonu\u00e7:<\/strong> Sorgu sonu\u00e7lar\u0131 h\u0131zland\u0131, yan\u0131tlar daha do\u011fru hale geldi. <\/li>\n
          5. \u0130\u015f etkisi:<\/strong> M\u00fc\u015fteri hizmetleri ekibi, s\u00f6zle\u015fme maddelerini saniyeler i\u00e7inde bulabiliyor; bilgi getirme s\u00fcreci otomatikle\u015fiyor. <\/li>\n<\/ol>\n
            \n

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

              Hatalar:<\/strong> <\/p>\n<\/li>\n

            • \n

              Par\u00e7a boyutlar\u0131n\u0131n kayna\u011fa g\u00f6re ayarlanmamas\u0131 <\/p>\n<\/li>\n

            • \n

              Paragraf s\u0131n\u0131rlar\u0131n\u0131n dikkate al\u0131nmamas\u0131 <\/p>\n<\/li>\n

            • \n

              Sadece karakter say\u0131s\u0131na g\u00f6re b\u00f6lme <\/p>\n<\/li>\n

            • \n

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

            • \n

              Dil, bi\u00e7im ve i\u00e7erik yap\u0131s\u0131na g\u00f6re splitting stratejisi belirle <\/p>\n<\/li>\n

            • \n

              Test veri setleriyle farkl\u0131 chunk size de\u011ferlerini dene <\/p>\n<\/li>\n

            • \n

              Vekt\u00f6r arama do\u011fruluk skorlar\u0131n\u0131 izleyerek parametreleri optimize et <\/p>\n<\/li>\n

            • \n

              S\u00fcreci n8n veya benzeri orkestrasyon sistemleriyle otomatikle\u015ftir <\/p>\n<\/li>\n<\/ul>\n

              \n

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

              Text splitting, RAG ve bilgi getirme s\u00fcre\u00e7lerinin temel teknik ad\u0131m\u0131d\u0131r. Do\u011fru uyguland\u0131\u011f\u0131nda hem sistem performans\u0131 hem de yan\u0131t kalitesi y\u00fckselir. NeKu.AI\u2019nin bilgi taban\u0131 \u00e7\u00f6z\u00fcmleri, bu yakla\u015f\u0131m\u0131 grounding mimarisi i\u00e7inde kullanarak kurumsal veri y\u00f6netimini daha ba\u011flamsal hale getirir. AI geli\u015ftiricileri ve veri m\u00fchendisleri i\u00e7in text splitting, modern bilgi eri\u015fim altyap\u0131s\u0131n\u0131n vazge\u00e7ilmez bile\u015fenidir.<\/p>","protected":false},"excerpt":{"rendered":"

              Text splitting nedir Giri\u015f Text splitting, b\u00fcy\u00fck metin verilerini daha k\u00fc\u00e7\u00fck ve anlaml\u0131 par\u00e7alara ay\u0131rma i\u015flemidir. RAG (Retrieval-Augmented Generation) ve bilgi getirme mimarilerinde bu i\u015flem, modelin [\u2026]<\/span><\/p>\n","protected":false},"author":2,"featured_media":515,"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-514","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry"],"yoast_head":"\nRAG sistemlerinde text splitting ile daha dogru bilgi erisimi - 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\/text-splitting-rag-sistemleri\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"RAG sistemlerinde text splitting ile daha dogru bilgi erisimi - NeKu.AI\" \/>\n<meta property=\"og:description\" content=\"Text splitting nedir Giri\u015f Text splitting, b\u00fcy\u00fck metin verilerini daha k\u00fc\u00e7\u00fck ve anlaml\u0131 par\u00e7alara ay\u0131rma i\u015flemidir. 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