Yapay zeka ajanlar\u0131n\u0131 (AI Agents) yerel bir geli\u015ftirme ortam\u0131nda veya bir Jupyter Notebook \u00fczerinde test ederken her \u015fey m\u00fckemmel g\u00f6r\u00fcn\u00fcr. Ajan\u0131n\u0131z sorulara do\u011fru yan\u0131tlar verir, karma\u015f\u0131k g\u00f6revleri yerine getirir ve adeta bir “dahi” gibi davran\u0131r. Ancak, bu ajanlar\u0131 ger\u00e7ek d\u00fcnya \u00fcretim ortam\u0131na (production) ta\u015f\u0131d\u0131\u011f\u0131n\u0131zda ve onlar\u0131 canl\u0131 bir veritaban\u0131na ba\u011flad\u0131\u011f\u0131n\u0131zda i\u015fler bir anda tuhafla\u015fmaya ba\u015flar. Ajanlar hal\u00fcsinasyon g\u00f6rmeye, eski verileri yeniymi\u015f gibi sunmaya veya veritaban\u0131ndaki binlerce kay\u0131t aras\u0131nda kaybolmaya ba\u015flar. Bu durum, modern yaz\u0131l\u0131m d\u00fcnyas\u0131nda “Veritaban\u0131, yapay zeka ajanlar\u0131n\u0131n tuhafla\u015ft\u0131\u011f\u0131 yerdir” (The database is where AI agents get weird) s\u00f6z\u00fcn\u00fcn do\u011fmas\u0131na neden olmu\u015ftur. Peki, bu tuhafl\u0131\u011f\u0131n arkas\u0131ndaki teknik sebepler nelerdir ve bu sorunlar\u0131 nas\u0131l a\u015fabiliriz?<\/p>\n
Bu makalede, yapay zeka ajanlar\u0131n\u0131n veritaban\u0131 etkile\u015fimlerinde ya\u015fad\u0131\u011f\u0131 temel zorluklar\u0131, vekt\u00f6r veritabanlar\u0131n\u0131n (vector databases) yaratt\u0131\u011f\u0131 yan\u0131lsamalar\u0131 ve veri tutarl\u0131l\u0131\u011f\u0131 problemlerini derinlemesine inceleyece\u011fiz. E\u011fer bir yapay zeka ajan\u0131 geli\u015ftiriyorsan\u0131z, veritaban\u0131 katman\u0131n\u0131n sadece bir depolama alan\u0131 de\u011fil, ajan\u0131n bili\u015fsel yeteneklerini do\u011frudan etkileyen bir “haf\u0131za merkezi” oldu\u011funu anlaman\u0131z gerekir. Bu haf\u0131za merkezi d\u00fczg\u00fcn y\u00f6netilmedi\u011finde, ajan\u0131n mant\u0131k y\u00fcr\u00fctme s\u00fcreci (reasoning) bozulur ve sonu\u00e7 olarak kullan\u0131c\u0131ya hatal\u0131 veya anlams\u0131z \u00e7\u0131kt\u0131lar sunulur.<\/p>\n
Bir yapay zeka ajan\u0131n\u0131n veritaban\u0131 ile ili\u015fkisini anlamak i\u00e7in \u00f6ncelikle bu ajanlar\u0131n nas\u0131l “hat\u0131rlad\u0131\u011f\u0131n\u0131” kavramak gerekir. \u0130nsanlarda oldu\u011fu gibi, yapay zeka modellerinde de k\u0131sa s\u00fcreli ve uzun s\u00fcreli bellek ayr\u0131m\u0131 mevcuttur. K\u0131sa s\u00fcreli bellek, genellikle modelin ba\u011flam penceresi (context window) ile s\u0131n\u0131rl\u0131d\u0131r. Ancak, ajan\u0131n ge\u00e7mi\u015fteki binlerce etkile\u015fimi veya devasa bir bilgi k\u00fcmesini hat\u0131rlamas\u0131 gerekti\u011finde devreye uzun s\u00fcreli bellek, yani veritabanlar\u0131 girer. Bu noktada ajan, veritaban\u0131ndan veri \u00e7ekmek i\u00e7in RAG (Retrieval-Augmented Generation – Veri Geri Getirme ile Zenginle\u015ftirilmi\u015f \u00dcretim) mimarisini kullan\u0131r.<\/p>\n
Veritaban\u0131 etkile\u015fimi ba\u015flad\u0131\u011f\u0131nda, ajan bir sorgu olu\u015fturur, veritaban\u0131ndan ilgili d\u00f6k\u00fcmanlar\u0131 \u00e7eker ve bu d\u00f6k\u00fcmanlar\u0131 kendi ba\u011flam\u0131na ekleyerek bir yan\u0131t \u00fcretir. Sorun \u015fu ki, veritabanlar\u0131 yap\u0131land\u0131r\u0131lm\u0131\u015f (SQL) veya yap\u0131land\u0131r\u0131lmam\u0131\u015f (NoSQL\/Vekt\u00f6r) verilerle doludur. Yapay zeka ajan\u0131, bu verilerin do\u011frulu\u011funu veya g\u00fcncelli\u011fini sorgulama yetene\u011fine her zaman sahip de\u011fildir. \u00d6rne\u011fin, bir m\u00fc\u015fteri temsilcisi ajan\u0131, veritaban\u0131nda “iptal edilmi\u015f” bir sipari\u015fi hala “aktif” olarak g\u00f6rebilir \u00e7\u00fcnk\u00fc veritaban\u0131 indeksi hen\u00fcz g\u00fcncellenmemi\u015ftir. Bu durum, ajan\u0131n kullan\u0131c\u0131ya yanl\u0131\u015f bilgi vermesine neden olur ve sistemin g\u00fcvenilirli\u011fini sarsar.<\/p>\n
Buna ek olarak, veritaban\u0131 sorgulama s\u00fcre\u00e7lerinde ya\u015fanan gecikmeler (latency), ajan\u0131n karar verme mekanizmas\u0131n\u0131 yava\u015flat\u0131r. Bir ajan, bir g\u00f6revi tamamlamak i\u00e7in birden fazla veritaban\u0131 sorgusu yapmak zorundaysa, bu sorgular\u0131n her biri bir “bekleme s\u00fcresi” yarat\u0131r. \u00dcretim ortam\u0131nda binlerce kullan\u0131c\u0131n\u0131n ayn\u0131 anda ajan\u0131 sorgulad\u0131\u011f\u0131n\u0131 d\u00fc\u015f\u00fcn\u00fcrsek, veritaban\u0131 darbo\u011fazlar\u0131 (bottlenecks) ka\u00e7\u0131n\u0131lmaz hale gelir. Bu durum, ajan\u0131n yan\u0131tlar\u0131n\u0131n tutars\u0131zla\u015fmas\u0131na ve hatta zaman a\u015f\u0131m\u0131 (timeout) hatalar\u0131 nedeniyle “sa\u00e7malamas\u0131na” yol a\u00e7ar.<\/p>\n
G\u00fcn\u00fcm\u00fczde yapay zeka ajanlar\u0131n\u0131n \u00e7o\u011fu, verileri vekt\u00f6rlere (say\u0131sal dizilere) d\u00f6n\u00fc\u015ft\u00fcrerek saklayan vekt\u00f6r veritabanlar\u0131n\u0131 kullan\u0131r. Vekt\u00f6r veritabanlar\u0131, “anlamsal benzerlik” (semantic similarity) ilkesine g\u00f6re \u00e7al\u0131\u015f\u0131r. Yani, “elma” kelimesi ile “meyve” kelimesi vekt\u00f6r uzay\u0131nda birbirine yak\u0131nd\u0131r. Ancak, bu benzerlik her zaman do\u011fruluk anlam\u0131na gelmez. \u00dcretim ortam\u0131nda, bir ajan veritaban\u0131ndan “en benzer” 5 d\u00f6k\u00fcman\u0131 getirmesini istedi\u011finde, veritaban\u0131 ona teknik olarak benzer ama ba\u011flamsal olarak tamamen yanl\u0131\u015f veriler sunabilir.<\/p>\n
\u00d6rne\u011fin, bir hukuk ajan\u0131 “i\u015f s\u00f6zle\u015fmesi feshi” hakk\u0131nda bir soru ald\u0131\u011f\u0131nda, vekt\u00f6r veritaban\u0131 ona “kira s\u00f6zle\u015fmesi feshi” ile ilgili belgeler getirebilir. \u00c7\u00fcnk\u00fc her iki belgede de “s\u00f6zle\u015fme” ve “fesih” kelimeleri yo\u011fun olarak ge\u00e7mektedir. Ajan, bu belgeleri do\u011fru bilgi kayna\u011f\u0131 olarak kabul eder ve kira hukuku kurallar\u0131n\u0131 i\u015f hukukuna uyarlayarak hatal\u0131 bir hukuki tavsiye verir. \u0130\u015fte bu, ajan\u0131n veritaban\u0131 y\u00fcz\u00fcnden “tuhafla\u015ft\u0131\u011f\u0131” en yayg\u0131n senaryolardan biridir. Bu sorunu a\u015fmak i\u00e7in sadece vekt\u00f6r aramas\u0131na g\u00fcvenmek yerine, hibrit arama (hybrid search) y\u00f6ntemleri kullan\u0131lmal\u0131d\u0131r.<\/p>\n
Hibrit arama, hem vekt\u00f6r benzerli\u011fini hem de geleneksel anahtar kelime (keyword) aramas\u0131n\u0131 birle\u015ftirir. B\u00f6ylece, hem anlamsal yak\u0131nl\u0131k hem de terim do\u011frulu\u011fu sa\u011flanm\u0131\u015f olur. \u00d6te yandan, veritaban\u0131ndaki verilerin “embedding” (g\u00f6mme) modelleri ile nas\u0131l temsil edildi\u011fi de kritiktir. E\u011fer \u00fcretim ortam\u0131nda kulland\u0131\u011f\u0131n\u0131z g\u00f6mme modeli ile verileri veritaban\u0131na kaydederken kulland\u0131\u011f\u0131n\u0131z model farkl\u0131ysa veya g\u00fcncellenmi\u015fse, ajan hi\u00e7bir veriyi do\u011fru \u015fekilde bulamaz. Bu, teknik ekiplerin \u00fcretim a\u015famas\u0131nda en s\u0131k yapt\u0131\u011f\u0131 hatalardan biridir.<\/p>\n
\u00dcretim ortam\u0131ndaki yapay zeka ajanlar\u0131 nadiren tek ba\u015flar\u0131na \u00e7al\u0131\u015f\u0131rlar. Genellikle ayn\u0131 veritaban\u0131na eri\u015fen birden fazla ajan veya geleneksel yaz\u0131l\u0131m servisi bulunur. Bu durum, “yar\u0131\u015f durumu” (race condition) ve veri tutarl\u0131l\u0131\u011f\u0131 (consistency) problemlerini beraberinde getirir. Bir ajan bir veriyi okurken, ba\u015fka bir servis o veriyi g\u00fcncelliyor olabilir. Geleneksel sistemlerde bu sorun “lock” (kilitleme) mekanizmalar\u0131yla \u00e7\u00f6z\u00fcl\u00fcrken, yapay zeka ajanlar\u0131 bu s\u00fcre\u00e7leri y\u00f6netmekte zorlanabilir.<\/p>\n
\u00d6zellikle uzun s\u00fcren i\u015flemler (long-running tasks) s\u0131ras\u0131nda ajan\u0131n veritaban\u0131ndan ald\u0131\u011f\u0131 bilgi bayatlayabilir (stale data). Ajan, i\u015flemin ba\u015f\u0131nda stokta olan bir \u00fcr\u00fcn\u00fc rezerve etmeye \u00e7al\u0131\u015f\u0131rken, i\u015flemin sonunda stok t\u00fckenmi\u015f olabilir. E\u011fer ajan bu de\u011fi\u015fikli\u011fi anl\u0131k olarak takip edemiyorsa, kullan\u0131c\u0131ya “\u00fcr\u00fcn rezerve edildi” deyip arka planda hata alabilir. Bu t\u00fcr senaryolar, ajan\u0131n g\u00fcvenilir bir i\u015f orta\u011f\u0131 olmaktan \u00e7\u0131k\u0131p bir “hata kayna\u011f\u0131na” d\u00f6n\u00fc\u015fmesine neden olur.<\/p>\n
Bu sorunu \u00e7\u00f6zmek i\u00e7in “Event-Driven Architecture” (Olay G\u00fcd\u00fcml\u00fc Mimari) kullan\u0131m\u0131 te\u015fvik edilmelidir. Veritaban\u0131ndaki her de\u011fi\u015fiklik bir olay (event) tetiklemeli ve ajan bu olaylar\u0131 dinleyerek kendi i\u00e7 durumunu (state) g\u00fcncellemelidir. Ayr\u0131ca, veritaban\u0131 i\u015flemlerinde “ACID” (Atomicity, Consistency, Isolation, Durability) prensiplerine sad\u0131k kalmak, ajanlar\u0131n tutarl\u0131 verilerle \u00e7al\u0131\u015fmas\u0131n\u0131 garanti alt\u0131na al\u0131r. A\u015fa\u011f\u0131daki kod \u00f6rne\u011finde, bir ajan\u0131n veritaban\u0131 g\u00fcncellemelerini nas\u0131l kontrol edebilece\u011fine dair basit bir Python mant\u0131\u011f\u0131 g\u00f6rebilirsiniz:<\/p>\n
\nimport sqlite3\n\ndef update_agent_memory(agent_id, key, value):\n try:\n conn = sqlite3.connect('agent_memory.db')\n cursor = conn.cursor()\n \n # Veriyi g\u00fcncellemeden \u00f6nce mevcut durumu kontrol et (Optimistic Concurrency Control)\n cursor.execute(\"SELECT version FROM memory WHERE agent_id = ? AND key = ?\", (agent_id, key))\n result = cursor.fetchone()\n \n if result:\n current_version = result[0]\n # S\u00fcr\u00fcm kontrol\u00fc yaparak g\u00fcncelliyoruz\n cursor.execute(\"UPDATE memory SET value = ?, version = version + 1 WHERE agent_id = ? AND key = ? AND version = ?\", \n (value, agent_id, key, current_version))\n \n if cursor.rowcount == 0:\n print(\"Hata: Veri ba\u015fka bir i\u015flem taraf\u0131ndan g\u00fcncellenmi\u015f!\")\n else:\n conn.commit()\n print(\"Haf\u0131za ba\u015far\u0131yla g\u00fcncellendi.\")\n \n conn.close()\n except Exception as e:\n print(f\"Veritaban\u0131 hatas\u0131: {e}\")\n\n# \u00d6rnek kullan\u0131m\nupdate_agent_memory(\"agent_007\", \"last_action\", \"order_processing\")\n<\/pre>\n<\/code>\n<\/div>\n
Vaka Analizi: E-Ticaret Destek Ajan\u0131n\u0131n \"Fiyat Karma\u015fas\u0131\"<\/h2>\n
Ger\u00e7ek bir d\u00fcnya senaryosuna g\u00f6z atal\u0131m. B\u00fcy\u00fck bir e-ticaret platformu, m\u00fc\u015fterilere \u00fcr\u00fcnler hakk\u0131nda bilgi veren ve sipari\u015f durumlar\u0131n\u0131 sorgulayan bir yapay zeka ajan\u0131 yay\u0131na ald\u0131. \u0130lk haftalarda her \u015fey yolunda gitti. Ancak, platform b\u00fcy\u00fck bir kampanya d\u00f6nemine girdi\u011finde ajan \"tuhafla\u015fmaya\" ba\u015flad\u0131. M\u00fc\u015fteriler, ajan\u0131n web sitesinde g\u00f6r\u00fcnen fiyattan farkl\u0131 fiyatlar s\u00f6yledi\u011fini veya t\u00fckenmi\u015f \u00fcr\u00fcnleri hala var gibi g\u00f6sterdi\u011fini raporlad\u0131.<\/p>\n
Yap\u0131lan teknik incelemede sorunun kayna\u011f\u0131n\u0131n vekt\u00f6r veritaban\u0131 indeksi oldu\u011fu anla\u015f\u0131ld\u0131. \u00dcr\u00fcn fiyatlar\u0131 ana SQL veritaban\u0131nda saniyeler i\u00e7inde g\u00fcncellenirken, vekt\u00f6r veritaban\u0131n\u0131n (RAG i\u00e7in kullan\u0131lan d\u00f6k\u00fcmanlar\u0131n) yeniden indekslenmesi saatler s\u00fcr\u00fcyordu. Ajan, anl\u0131k fiyat\u0131 SQL'den \u00e7ekmek yerine, vekt\u00f6r veritaban\u0131ndaki eski d\u00f6k\u00fcmanlardan (embedding'lerden) okuyordu. Bu durum, ajan\u0131n \"bayat veri\" (stale data) \u00fczerinden hal\u00fcsinasyon g\u00f6rmesine neden oluyordu.<\/p>\n
\u00c7\u00f6z\u00fcm olarak, m\u00fchendislik ekibi iki a\u015famal\u0131 bir do\u011frulama sistemi kurdu. Ajan, vekt\u00f6r veritaban\u0131ndan genel \u00fcr\u00fcn bilgisini getirdikten sonra, kritik verileri (fiyat, stok) do\u011frulamak i\u00e7in ana SQL veritaban\u0131na bir \"point query\" (nokta sorgusu) yapmaya ba\u015flad\u0131. Bu vaka, yapay zeka ajanlar\u0131n\u0131n sadece vekt\u00f6r veritabanlar\u0131na emanet edilemeyece\u011fini, geleneksel veritabanlar\u0131yla s\u0131k\u0131 bir entegrasyonun \u015fart oldu\u011funu kan\u0131tlamaktad\u0131r. Ajanlar\u0131n \u00fcretimde sa\u00e7malamas\u0131n\u0131 istemiyorsan\u0131z, onlara her zaman \"ger\u00e7e\u011fin tek kayna\u011f\u0131n\u0131\" (single source of truth) g\u00f6stermelisiniz.<\/p>\n
\u0130leri D\u00fczey \u0130pucu: Metadata Filtreleme ve Re-ranking Kullan\u0131m\u0131<\/h2>\n
Deneyimli bir geli\u015ftiriciyseniz, sadece RAG kullanman\u0131n yeterli olmad\u0131\u011f\u0131n\u0131 bilirsiniz. Ajan\u0131n veritaban\u0131nda kaybolmas\u0131n\u0131 engellemek i\u00e7in \"Metadata Filtering\" (Meta Veri Filtreleme) tekniklerini kullanmal\u0131s\u0131n\u0131z. Bir sorgu yaparken sadece benzerli\u011fe bakmak yerine, \"Sadece son 24 saatteki verileri getir\" veya \"Sadece 'elektronik' kategorisindeki belgeleri tara\" gibi k\u0131s\u0131tlamalar eklemek, ajan\u0131n hata pay\u0131n\u0131 dramatik \u015fekilde d\u00fc\u015f\u00fcr\u00fcr.<\/p>\n
Buna ek olarak, \"Re-ranking\" (Yeniden S\u0131ralama) modelleri kullanmak, veritaban\u0131ndan d\u00f6nen sonu\u00e7lar\u0131n kalitesini art\u0131r\u0131r. Vekt\u00f6r veritaban\u0131 size 20 tane sonu\u00e7 d\u00f6nd\u00fcrebilir, ancak bu sonu\u00e7lar\u0131n hepsi ajan\u0131n sorusuna yan\u0131t vermek i\u00e7in uygun olmayabilir. Bir re-ranking modeli (\u00f6rne\u011fin Cohere Rerank veya Cross-Encoders), bu 20 sonucu tekrar analiz eder ve en alakal\u0131 3 sonucu ajana sunar. Bu i\u015flem, ajan\u0131n ba\u011flam penceresine giren \"g\u00fcr\u00fclt\u00fcy\u00fc\" (noise) azalt\u0131r ve daha tutarl\u0131 yan\u0131tlar \u00fcretmesini sa\u011flar.<\/p>\n
\u00d6zellikle b\u00fcy\u00fck \u00f6l\u00e7ekli sistemlerde, veritaban\u0131 \u015femas\u0131n\u0131n ajan taraf\u0131ndan anla\u015f\u0131labilir olmas\u0131 da kritiktir. E\u011fer ajan SQL sorgular\u0131n\u0131 kendisi \u00fcretiyorsa (Text-to-SQL), veritaban\u0131 tablolar\u0131n\u0131z\u0131n isimleri ve s\u00fctun a\u00e7\u0131klamalar\u0131 \u00e7ok net olmal\u0131d\u0131r. usr_t_01<\/code> gibi bir tablo ismi yerine user_transactions<\/code> gibi a\u00e7\u0131klay\u0131c\u0131 isimler kullanmak, ajan\u0131n do\u011fru tabloyu se\u00e7me olas\u0131l\u0131\u011f\u0131n\u0131 art\u0131r\u0131r. Unutmay\u0131n, ajanlar sizin kodunuzu de\u011fil, verinizin yap\u0131s\u0131n\u0131 okuyarak d\u00fcnyay\u0131 anlamland\u0131r\u0131r.<\/p>\nSonu\u00e7: Veritaban\u0131 Ajan\u0131n Aynas\u0131d\u0131r<\/h2>\n
Sonu\u00e7 olarak, yapay zeka ajanlar\u0131n\u0131n \u00fcretim ortam\u0131nda sergiledi\u011fi tuhaf davran\u0131\u015flar\u0131n \u00e7o\u011fu, veritaban\u0131 katman\u0131ndaki kopukluklardan kaynaklan\u0131r. Veri g\u00fcncelli\u011fi, e\u015fzamanl\u0131l\u0131k sorunlar\u0131, yanl\u0131\u015f embedding modelleri ve yetersiz filtreleme, en geli\u015fmi\u015f LLM'leri (B\u00fcy\u00fck Dil Modelleri) bile i\u015flevsiz hale getirebilir. Bir ajan geli\u015ftirirken vaktinizin %20'sini model ayarlar\u0131na (fine-tuning), %80'ini ise veri y\u00f6netimine ve veritaban\u0131 mimarisine harcaman\u0131z gerekir.<\/p>\n
Veritaban\u0131, bir ajan\u0131n d\u0131\u015f d\u00fcnya ile kurdu\u011fu en g\u00fc\u00e7l\u00fc ba\u011fd\u0131r. Bu ba\u011f\u0131n kopmas\u0131 veya zay\u0131flamas\u0131, ajan\u0131n ger\u00e7eklikten kopmas\u0131na yol a\u00e7ar. \u00dcretim ortam\u0131nda ba\u015far\u0131l\u0131 bir yapay zeka sistemi kurmak istiyorsan\u0131z, veritaban\u0131n\u0131z\u0131 sadece bir depolama birimi olarak de\u011fil, ajan\u0131n bili\u015fsel s\u00fcre\u00e7lerini besleyen dinamik bir ekosistem olarak tasarlamal\u0131s\u0131n\u0131z. Ancak bu \u015fekilde \"tuhafla\u015fan\" ajanlar yerine, kullan\u0131c\u0131lar\u0131na ger\u00e7ekten de\u011fer katan ak\u0131ll\u0131 sistemler in\u015fa edebilirsiniz.<\/p>\n
S\u0131k\u00e7a Sorulan Sorular<\/h3>\n\n- Ajan\u0131m neden s\u00fcrekli eski bilgileri veriyor?<\/strong> Muhtemelen vekt\u00f6r veritaban\u0131 indeksiniz ana veritaban\u0131n\u0131zla senkronize de\u011fil. RAG sistemlerinde veri g\u00fcncellendi\u011finde embedding'lerin de anl\u0131k veya periyodik olarak yenilenmesi gerekir.<\/li>\n
- Vekt\u00f6r veritaban\u0131 m\u0131 yoksa SQL mi kullanmal\u0131y\u0131m?<\/strong> \u0130deal bir yapay zeka ajan\u0131 her ikisini de kullanmal\u0131d\u0131r. Vekt\u00f6r veritabanlar\u0131 anlamsal arama i\u00e7in, SQL ise kesin veriler (fiyat, tarih, stok) i\u00e7in gereklidir.<\/li>\n
- Hal\u00fcsinasyonlar\u0131 veritaban\u0131 ile nas\u0131l engellerim?<\/strong> \"Grounding\" (Temellendirme) tekni\u011fini kullanarak, ajana sadece veritaban\u0131ndan gelen d\u00f6k\u00fcmanlara dayanarak cevap vermesi gerekti\u011fini, d\u00f6k\u00fcmanda olmayan bilgiyi uydurmamas\u0131 gerekti\u011fini kesin bir dille belirtmelisiniz.<\/li>\n
- Veritaban\u0131 gecikmesi ajan\u0131 nas\u0131l etkiler?<\/strong> Uzun gecikmeler ajan\u0131n i\u015flem s\u0131ras\u0131n\u0131 bozabilir. Ajan, yan\u0131t beklerken zaman a\u015f\u0131m\u0131na u\u011frayabilir veya eksik veriyle i\u015fleme devam ederek hatal\u0131 sonu\u00e7lar \u00fcretebilir.<\/li>\n<\/ul>\n
#YapayZeka #Veritaban\u0131 #AI #Yaz\u0131l\u0131mGeli\u015ftirme #RAG #Vekt\u00f6rVeritaban\u0131<\/p>\n
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