LangChain: \u0130lk LLM Uygulaman\u0131z\u0131 Geli\u015ftirme<\/title><\/p>\n<style>\n body { font-family: sans-serif; line-height: 1.6; }\n h1, h2, h3 { font-weight: bold; }\n code { background-color: #f0f0f0; padding: 0.2em 0.4em; border-radius: 3px; }\n pre { background-color: #f0f0f0; padding: 1em; border-radius: 5px; overflow-x: auto; }\n .warning { background-color: #ffffe0; border: 1px solid #ffdd99; padding: 1em; margin-bottom: 1em; }\n @media (max-width: 768px) {\n body { font-size: 16px; }\n }\n <\/style>\n<p><\/head><br \/>\n<body><\/p>\n<p> LangChain: \u0130lk LLM Uygulaman\u0131z\u0131 Geli\u015ftirme<\/p>\n<p>B\u00fcy\u00fck dil modelleri (LLM'ler) art\u0131k hayat\u0131m\u0131z\u0131n her alan\u0131nda yer al\u0131yor. Ancak bu g\u00fc\u00e7l\u00fc ara\u00e7lar\u0131 etkin bir \u015fekilde kullanmak i\u00e7in do\u011fru ara\u00e7lara ve bilgiye sahip olmak gerekiyor. \u0130\u015fte burada LangChain devreye giriyor. Bu makalede, LangChain'i kullanarak kendi LLM tabanl\u0131 uygulaman\u0131z\u0131 nas\u0131l olu\u015fturaca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz. Temel kavramlardan ba\u015flayarak ileri tekniklere kadar, ger\u00e7ek d\u00fcnya \u00f6rnekleriyle desteklenmi\u015f kapsaml\u0131 bir yol haritas\u0131 sunaca\u011f\u0131z.<\/p>\n<h2>LangChain'e Giri\u015f: Nedir ve Neden Kullanmal\u0131y\u0131z?<\/h2>\n<p>LangChain, b\u00fcy\u00fck dil modellerini (LLM'ler) uygulamalara entegre etmeyi kolayla\u015ft\u0131ran g\u00fc\u00e7l\u00fc bir Python k\u00fct\u00fcphanesidir. LLM'ler, OpenAI'nin GPT modelleri, Google'\u0131n PaLM gibi karma\u015f\u0131k g\u00f6revleri yerine getirebilen geli\u015fmi\u015f dil modelleri olarak tan\u0131mlanabilir. LangChain'in en b\u00fcy\u00fck avantaj\u0131, bu LLM'leri \u00e7e\u015fitli veri kaynaklar\u0131 ve uygulamalarla sorunsuz bir \u015fekilde birle\u015ftirmesidir. Bu sayede, karma\u015f\u0131k ve etkileyici uygulamalar olu\u015fturmak \u00e7ok daha kolay ve h\u0131zl\u0131 hale gelir. \u00d6rne\u011fin, bir chatbot olu\u015fturmak, bir belgeyi \u00f6zetlemek veya bir veri taban\u0131 sorgusunu do\u011fal dil ile y\u00f6netmek i\u00e7in LangChain'i kullanabilirsiniz. LangChain'in mimarisi, mod\u00fcler bir yakla\u015f\u0131m sunarak, ihtiya\u00e7lar\u0131n\u0131za g\u00f6re \u00f6zelle\u015ftirilebilir ve geni\u015fletilebilir \u00e7\u00f6z\u00fcmler olu\u015fturman\u0131za olanak tan\u0131r. Bu mod\u00fclerlik, farkl\u0131 LLM'ler, veri kaynaklar\u0131 ve i\u015f ak\u0131\u015flar\u0131 aras\u0131nda kolayca ge\u00e7i\u015f yapman\u0131za yard\u0131mc\u0131 olur. Ayr\u0131ca, LangChain, farkl\u0131 LLM sa\u011flay\u0131c\u0131lar\u0131 ile uyumluluk sa\u011flayarak, esneklik sunmaktad\u0131r. Daha fazla bilgi i\u00e7in <a href=\"https:\/\/fatihsoysal.com\">fatihsoysal.com<\/a> sitesini ziyaret edebilirsiniz.<\/p>\n<h2>LangChain'in Temel Bile\u015fenleri: Mod\u00fcller ve \u0130\u015f Ak\u0131\u015flar\u0131<\/h2>\n<p>LangChain, temel olarak \u00fc\u00e7 ana bile\u015fenden olu\u015fur: Modeller, Prompts ve Chains. <b>Modeller<\/b>, GPT-3, LLaMA gibi b\u00fcy\u00fck dil modellerini temsil eder. LangChain, bu modelleri kolayca entegre etmenizi sa\u011flar. <b>Prompts<\/b>, LLM'lere soraca\u011f\u0131n\u0131z sorular\u0131 veya verece\u011finiz talimatlar\u0131 tan\u0131mlar. \u0130yi tasarlanm\u0131\u015f bir prompt, daha do\u011fru ve alakal\u0131 yan\u0131tlar alman\u0131za yard\u0131mc\u0131 olur. <b>Chains<\/b> ise, modelleri ve promptlar\u0131 bir araya getirerek karma\u015f\u0131k i\u015f ak\u0131\u015flar\u0131 olu\u015fturman\u0131za imkan tan\u0131r. Bir Chain, birden fazla modeli ve prompt'u s\u0131rayla \u00e7al\u0131\u015ft\u0131rabilir ve bunlar\u0131n \u00e7\u0131kt\u0131lar\u0131na g\u00f6re kararlar alabilir. \u00d6rne\u011fin, bir belge \u00f6zetleme chain'i \u00f6nce belgeyi par\u00e7alara ay\u0131r\u0131r, daha sonra her par\u00e7ay\u0131 ayr\u0131 ayr\u0131 modele g\u00f6nderir ve son olarak \u00f6zetlenmi\u015f par\u00e7alar\u0131 birle\u015ftirerek tam bir \u00f6zeti olu\u015fturur. LangChain'in mod\u00fcler yap\u0131s\u0131 sayesinde, bu bile\u015fenleri istedi\u011finiz \u015fekilde birle\u015ftirebilir ve kendi \u00f6zel i\u015f ak\u0131\u015flar\u0131n\u0131z\u0131 olu\u015fturabilirsiniz. Bu esneklik, \u00e7e\u015fitli uygulamalar\u0131 desteklemek i\u00e7in olduk\u00e7a \u00f6nemlidir.<\/p>\n<h2>\u0130lk LangChain Uygulaman\u0131z: Basit Bir Chatbot<\/h2>\n<p>\u015eimdi, LangChain kullanarak basit bir chatbot olu\u015ftural\u0131m. Bu \u00f6rnekte, OpenAI'nin GPT-3.5-turbo modelini kullanaca\u011f\u0131z. \u00d6ncelikle gerekli k\u00fct\u00fcphaneleri kurman\u0131z gerekmektedir:<\/p>\n<p> <code><br \/>\n pip install langchain openai<br \/>\n <\/code><\/p>\n<p>Ard\u0131ndan, a\u015fa\u011f\u0131daki kodu \u00e7al\u0131\u015ft\u0131rabilirsiniz:<\/p>\n<pre>\n <code>\n from langchain.llms import OpenAI\n from langchain.prompts import PromptTemplate\n from langchain.chains import LLMChain\n\n # OpenAI API anahtar\u0131n\u0131z\u0131 burada belirtin\n import os\n os.environ[\"OPENAI_API_KEY\"] = \"YOUR_API_KEY\"\n\n llm = OpenAI(temperature=0)\n prompt_template = \"Soru: {question}\"\n prompt = PromptTemplate(input_variables=[\"question\"], template=prompt_template)\n chain = LLMChain(llm=llm, prompt=prompt)\n\n while True:\n user_input = input(\"Sorunuzu girin (\u00e7\u0131kmak i\u00e7in '\u00e7\u0131k\u0131\u015f' yaz\u0131n): \")\n if user_input.lower() == \"\u00e7\u0131k\u0131\u015f\":\n break\n response = chain.run(user_input)\n print(response)\n <\/code>\n <\/pre>\n<p>Bu kod, kullan\u0131c\u0131n\u0131n sorular\u0131n\u0131 OpenAI'nin GPT-3.5-turbo modeline iletmekte ve ald\u0131\u011f\u0131 cevab\u0131 ekranda g\u00f6stermektedir. “YOUR_API_KEY” yerine kendi OpenAI API anahtar\u0131n\u0131z\u0131 girmeyi unutmay\u0131n. Bu basit \u00f6rnek, LangChain'in ne kadar kolay kullan\u0131labilece\u011fini g\u00f6stermektedir. Daha geli\u015fmi\u015f chatbotlar i\u00e7in, daha karma\u015f\u0131k prompt'lar ve zincirlemeler kullanabilirsiniz.<\/p>\n<h2>Daha Geli\u015fmi\u015f LangChain Uygulamalar\u0131: Ger\u00e7ek D\u00fcnya Senaryolar\u0131<\/h2>\n<p>LangChain, basit chatbotlardan \u00e7ok daha karma\u015f\u0131k uygulamalar olu\u015fturman\u0131za imkan tan\u0131r. \u0130\u015fte baz\u0131 ger\u00e7ek d\u00fcnya senaryolar\u0131:<\/p>\n<h3>1. Belge \u00d6zetleme:<\/h3>\n<p>LangChain, b\u00fcy\u00fck belgeleri otomatik olarak \u00f6zetlemenizi sa\u011flar. \u00d6rne\u011fin, bir PDF dosyas\u0131n\u0131 y\u00fckleyerek, LangChain'i kullanarak ana noktalar\u0131 \u00f6zetleyebilirsiniz. Bu, ara\u015ft\u0131rma, bilgi toplama ve analiz gibi bir\u00e7ok alanda zaman kazand\u0131r\u0131r.<\/p>\n<h3>2. Veri Taban\u0131 Sorgular\u0131:<\/h3>\n<p>LangChain, do\u011fal dil sorgulamalar\u0131 kullanarak veri tabanlar\u0131na eri\u015fmenizi sa\u011flar. Kullan\u0131c\u0131lar\u0131n karma\u015f\u0131k SQL sorgular\u0131 yazmas\u0131na gerek kalmadan, do\u011fal dil ile veri taban\u0131ndan bilgi almalar\u0131n\u0131 m\u00fcmk\u00fcn k\u0131lar. Bu, veri analizi ve raporlama i\u015flemlerini kolayla\u015ft\u0131r\u0131r.<\/p>\n<h3>3. \u00c7oklu Dil Deste\u011fi:<\/h3>\n<p>LangChain, farkl\u0131 dilleri destekleyen LLM'lerle \u00e7al\u0131\u015fabilir. Bu \u00f6zellik, uluslararas\u0131 pazarlara ula\u015fmak ve farkl\u0131 dil gruplar\u0131na hizmet vermek isteyen uygulamalar i\u00e7in olduk\u00e7a \u00f6nemlidir. \u00c7eviri i\u015flemleri ve \u00e7ok dilli chatbot geli\u015ftirme gibi alanlarda kullan\u0131labilir.<\/p>\n<h2>LangChain ile Performans\u0131 Optimize Etme: \u0130pu\u00e7lar\u0131 ve P\u00fcf Noktalar\u0131<\/h2>\n<p>LangChain ile uygulamalar\u0131n\u0131z\u0131 geli\u015ftirirken performans\u0131 optimize etmek i\u00e7in baz\u0131 \u00f6nemli ipu\u00e7lar\u0131 \u015funlard\u0131r:<\/p>\n<ul>\n<li><b>Uygun Prompts'lar Tasarlay\u0131n:<\/b> Net ve \u00f6zl\u00fc prompt'lar kullanarak LLM'lerin daha do\u011fru ve verimli yan\u0131tlar \u00fcretmesini sa\u011flayabilirsiniz.<\/li>\n<li><b>Cachelemeyi Kullan\u0131n:<\/b> Daha \u00f6nce cevaplanm\u0131\u015f olan sorular\u0131n \u00f6nbelle\u011fe al\u0131nmas\u0131, tekrarlanan istekleri azaltarak performans\u0131 art\u0131r\u0131r.<\/li>\n<li><b>Paralelle\u015ftirmeyi Uygulay\u0131n:<\/b> LangChain'in paralel i\u015fleme yeteneklerini kullanarak, i\u015flemlerinizi h\u0131zland\u0131rabilirsiniz. \u00d6zellikle b\u00fcy\u00fck veri setleri ile \u00e7al\u0131\u015f\u0131rken paralellik \u00f6nemli bir performans iyile\u015ftirmesi sa\u011flar.<\/li>\n<li><b>Veri \u00d6n \u0130\u015fleme:<\/b> Verilerinizi \u00f6nceden i\u015flemek, LLM'lerin daha h\u0131zl\u0131 \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flar. \u00d6rne\u011fin, b\u00fcy\u00fck metinleri par\u00e7alara ay\u0131rmak veya veri temizleme i\u015flemleri yaparak performans\u0131 art\u0131rabilirsiniz.<\/li>\n<\/ul>\n<h2>\u00d6\u011frenme Yol Haritas\u0131<\/h2>\n<h3>Yeni Ba\u015flayanlar \u0130\u00e7in:<\/h3>\n<p>LangChain'e giri\u015f yaparken, temel kavramlar\u0131 \u00f6\u011frenerek ba\u015flay\u0131n. Basit bir chatbot \u00f6rne\u011fiyle pratik yap\u0131n ve farkl\u0131 LLM'leri deneyin. Daha sonra, LangChain dok\u00fcmantasyonunu inceleyerek daha fazla bilgi edinebilirsiniz.<\/p>\n<h3>Orta Seviye:<\/h3>\n<p>Daha geli\u015fmi\u015f uygulamalar geli\u015ftirmeye odaklan\u0131n. Belge \u00f6zetleme, veri taban\u0131 sorgulama gibi ger\u00e7ek d\u00fcnya senaryolar\u0131n\u0131 deneyin. Farkl\u0131 zincirleme (chains) tiplerini ke\u015ffederek uygulamalar\u0131n\u0131z\u0131 daha da geli\u015ftirin.<\/p>\n<h3>\u0130leri Seviye:<\/h3>\n<p>Performans optimizasyonuna odaklan\u0131n. Paralelle\u015ftirme tekniklerini uygulay\u0131n ve kendi \u00f6zel zincirlemelerinizi olu\u015fturun. LangChain'i farkl\u0131 veri kaynaklar\u0131yla entegre edin ve karma\u015f\u0131k uygulamalar geli\u015ftirin. Ayr\u0131ca, LangChain'in geli\u015fmi\u015f \u00f6zelliklerini (\u00f6rne\u011fin, agent'ler) ara\u015ft\u0131r\u0131n.<\/p>\n<h2>Sonu\u00e7<\/h2>\n<p>LangChain, b\u00fcy\u00fck dil modellerini uygulamalar\u0131n\u0131za entegre etmenizi kolayla\u015ft\u0131ran g\u00fc\u00e7l\u00fc bir ara\u00e7t\u0131r. Bu makalede, LangChain'in temel kavramlar\u0131n\u0131, kullan\u0131m\u0131n\u0131 ve ileri tekniklerini \u00f6\u011frendiniz. Ger\u00e7ek d\u00fcnya \u00f6rnekleri ve ipu\u00e7lar\u0131yla, kendi LLM tabanl\u0131 uygulamalar\u0131n\u0131z\u0131 geli\u015ftirmeye ba\u015flayabilirsiniz. Daha fazla bilgi ve \u00f6rnekler i\u00e7in LangChain'in resmi dok\u00fcmantasyonuna ve <a href=\"https:\/\/fatihsoysal.com\">fatihsoysal.com<\/a> adresine g\u00f6z atabilirsiniz.<\/p>\n<h2>S\u0131k\u00e7a Sorulan Sorular<\/h2>\n<ul>\n<li><b>LangChain hangi programlama dillerini destekler?<\/b> LangChain, \u015fu anda \u00f6ncelikle Python'\u0131 desteklemektedir.<\/li>\n<li><b>LangChain hangi LLM'leri destekler?<\/b> LangChain, OpenAI, Google AI, Hugging Face gibi bir\u00e7ok LLM sa\u011flay\u0131c\u0131s\u0131yla uyumludur.<\/li>\n<li><b>LangChain'in \u00f6\u011frenme e\u011frisi nas\u0131l?<\/b> LangChain'in \u00f6\u011frenme e\u011frisi, temel kavramlar\u0131 kavrad\u0131ktan sonra nispeten kolayd\u0131r. \u0130yi haz\u0131rlanm\u0131\u015f dok\u00fcmantasyon ve \u00f6rnekler mevcuttur.<\/li>\n<li><b>LangChain \u00fccretsiz mi?<\/b> LangChain a\u00e7\u0131k kaynakl\u0131 bir projedir ve \u00fccretsiz olarak kullan\u0131labilir. Ancak, kulland\u0131\u011f\u0131n\u0131z LLM'lerin \u00fccretlendirmesi olabilir.<\/li>\n<li><b>LangChain g\u00fcvenli mi?<\/b>\n<div class=\"warning\"><b>Dikkat:<\/b> LangChain'i kullan\u0131rken, kullan\u0131c\u0131lardan al\u0131nan verilerin g\u00fcvenli\u011fini sa\u011flamak ve zararl\u0131 i\u00e7eriklerin engellenmesi i\u00e7in gerekli \u00f6nlemleri alman\u0131z \u00f6nemlidir. Uygulaman\u0131z\u0131n g\u00fcvenlik a\u00e7\u0131klar\u0131n\u0131 d\u00fczenli olarak kontrol etmelisiniz. <\/div>\n<\/li>\n<\/ul>\n<p>Yazar: Fatih Soysal<\/p>\n<p><\/body><br \/>\n<\/html><\/code><\/p>\n","protected":false},"excerpt":{"rendered":"LangChain: \u0130lk LLM Uygulaman\u0131z\u0131 Geli\u015ftirme LangChain: \u0130lk LLM Uygulaman\u0131z\u0131 Geli\u015ftirme B\u00fcy\u00fck dil modelleri (LLM'ler) art\u0131k hayat\u0131m\u0131z\u0131n her alan\u0131nda…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-29155","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n<title>LangChain: \u0130lk LLM Uygulaman\u0131z\u0131 Geli\u015ftirme<\/title>\n<meta name=\"description\" content=\"B\u00fcy\u00fck dil modelleri (LLM'ler) art\u0131k hayat\u0131m\u0131z\u0131n her alan\u0131nda yer al\u0131yor. Ancak bu g\u00fc\u00e7l\u00fc ara\u00e7lar\u0131 etkin bir \u015fekilde kullanmak i\u00e7in do\u011fru ara\u00e7lara ve bilgiye sahip olmak gerekiyor. \u0130\u015fte burada LangChain devreye giriyor. Bu makalede, LangChain'i kullanarak kendi LLM tabanl\u0131 uygulaman\u0131z\u0131 nas\u0131l olu\u015fturaca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz. Temel kavramlardan ba\u015flayarak ileri tekniklere kadar, ger\u00e7ek d\u00fcnya \u00f6rnekleriyle desteklenmi\u015f kapsaml\u0131 bir yol haritas\u0131 sunaca\u011f\u0131z.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/langchain-ilk-llm-uygulamanizi-gelistirme\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LangChain: \u0130lk LLM Uygulaman\u0131z\u0131 Geli\u015ftirme\" \/>\n<meta property=\"og:description\" content=\"B\u00fcy\u00fck dil modelleri (LLM'ler) art\u0131k hayat\u0131m\u0131z\u0131n her alan\u0131nda yer al\u0131yor. Ancak bu g\u00fc\u00e7l\u00fc ara\u00e7lar\u0131 etkin bir \u015fekilde kullanmak i\u00e7in do\u011fru ara\u00e7lara ve bilgiye sahip olmak gerekiyor. \u0130\u015fte burada LangChain devreye giriyor. Bu makalede, LangChain'i kullanarak kendi LLM tabanl\u0131 uygulaman\u0131z\u0131 nas\u0131l olu\u015fturaca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz. 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{"id":29155,"date":"2025-09-08T13:30:24","date_gmt":"2025-09-08T10:30:24","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/langchain-ilk-llm-uygulamanizi-gelistirme\/"},"modified":"2025-09-08T13:30:24","modified_gmt":"2025-09-08T10:30:24","slug":"langchain-ilk-llm-uygulamanizi-gelistirme","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/langchain-ilk-llm-uygulamanizi-gelistirme\/","title":{"rendered":"LangChain: \u0130lk LLM Uygulaman\u0131z\u0131 Geli\u015ftirme"},"content":{"rendered":"

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\n LangChain: \u0130lk LLM Uygulaman\u0131z\u0131 Geli\u015ftirme<\/title><\/p>\n<style>\n body { font-family: sans-serif; line-height: 1.6; }\n h1, h2, h3 { font-weight: bold; }\n code { background-color: #f0f0f0; padding: 0.2em 0.4em; border-radius: 3px; }\n pre { background-color: #f0f0f0; padding: 1em; border-radius: 5px; overflow-x: auto; }\n .warning { background-color: #ffffe0; border: 1px solid #ffdd99; padding: 1em; margin-bottom: 1em; }\n @media (max-width: 768px) {\n body { font-size: 16px; }\n }\n <\/style>\n<p><\/head><br \/>\n<body><\/p>\n<p> LangChain: \u0130lk LLM Uygulaman\u0131z\u0131 Geli\u015ftirme<\/p>\n<p>B\u00fcy\u00fck dil modelleri (LLM'ler) art\u0131k hayat\u0131m\u0131z\u0131n her alan\u0131nda yer al\u0131yor. Ancak bu g\u00fc\u00e7l\u00fc ara\u00e7lar\u0131 etkin bir \u015fekilde kullanmak i\u00e7in do\u011fru ara\u00e7lara ve bilgiye sahip olmak gerekiyor. \u0130\u015fte burada LangChain devreye giriyor. Bu makalede, LangChain'i kullanarak kendi LLM tabanl\u0131 uygulaman\u0131z\u0131 nas\u0131l olu\u015fturaca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz. Temel kavramlardan ba\u015flayarak ileri tekniklere kadar, ger\u00e7ek d\u00fcnya \u00f6rnekleriyle desteklenmi\u015f kapsaml\u0131 bir yol haritas\u0131 sunaca\u011f\u0131z.<\/p>\n<h2>LangChain'e Giri\u015f: Nedir ve Neden Kullanmal\u0131y\u0131z?<\/h2>\n<p>LangChain, b\u00fcy\u00fck dil modellerini (LLM'ler) uygulamalara entegre etmeyi kolayla\u015ft\u0131ran g\u00fc\u00e7l\u00fc bir Python k\u00fct\u00fcphanesidir. LLM'ler, OpenAI'nin GPT modelleri, Google'\u0131n PaLM gibi karma\u015f\u0131k g\u00f6revleri yerine getirebilen geli\u015fmi\u015f dil modelleri olarak tan\u0131mlanabilir. LangChain'in en b\u00fcy\u00fck avantaj\u0131, bu LLM'leri \u00e7e\u015fitli veri kaynaklar\u0131 ve uygulamalarla sorunsuz bir \u015fekilde birle\u015ftirmesidir. Bu sayede, karma\u015f\u0131k ve etkileyici uygulamalar olu\u015fturmak \u00e7ok daha kolay ve h\u0131zl\u0131 hale gelir. \u00d6rne\u011fin, bir chatbot olu\u015fturmak, bir belgeyi \u00f6zetlemek veya bir veri taban\u0131 sorgusunu do\u011fal dil ile y\u00f6netmek i\u00e7in LangChain'i kullanabilirsiniz. LangChain'in mimarisi, mod\u00fcler bir yakla\u015f\u0131m sunarak, ihtiya\u00e7lar\u0131n\u0131za g\u00f6re \u00f6zelle\u015ftirilebilir ve geni\u015fletilebilir \u00e7\u00f6z\u00fcmler olu\u015fturman\u0131za olanak tan\u0131r. Bu mod\u00fclerlik, farkl\u0131 LLM'ler, veri kaynaklar\u0131 ve i\u015f ak\u0131\u015flar\u0131 aras\u0131nda kolayca ge\u00e7i\u015f yapman\u0131za yard\u0131mc\u0131 olur. Ayr\u0131ca, LangChain, farkl\u0131 LLM sa\u011flay\u0131c\u0131lar\u0131 ile uyumluluk sa\u011flayarak, esneklik sunmaktad\u0131r. Daha fazla bilgi i\u00e7in <a href=\"https:\/\/fatihsoysal.com\">fatihsoysal.com<\/a> sitesini ziyaret edebilirsiniz.<\/p>\n<h2>LangChain'in Temel Bile\u015fenleri: Mod\u00fcller ve \u0130\u015f Ak\u0131\u015flar\u0131<\/h2>\n<p>LangChain, temel olarak \u00fc\u00e7 ana bile\u015fenden olu\u015fur: Modeller, Prompts ve Chains. <b>Modeller<\/b>, GPT-3, LLaMA gibi b\u00fcy\u00fck dil modellerini temsil eder. LangChain, bu modelleri kolayca entegre etmenizi sa\u011flar. <b>Prompts<\/b>, LLM'lere soraca\u011f\u0131n\u0131z sorular\u0131 veya verece\u011finiz talimatlar\u0131 tan\u0131mlar. \u0130yi tasarlanm\u0131\u015f bir prompt, daha do\u011fru ve alakal\u0131 yan\u0131tlar alman\u0131za yard\u0131mc\u0131 olur. <b>Chains<\/b> ise, modelleri ve promptlar\u0131 bir araya getirerek karma\u015f\u0131k i\u015f ak\u0131\u015flar\u0131 olu\u015fturman\u0131za imkan tan\u0131r. Bir Chain, birden fazla modeli ve prompt'u s\u0131rayla \u00e7al\u0131\u015ft\u0131rabilir ve bunlar\u0131n \u00e7\u0131kt\u0131lar\u0131na g\u00f6re kararlar alabilir. \u00d6rne\u011fin, bir belge \u00f6zetleme chain'i \u00f6nce belgeyi par\u00e7alara ay\u0131r\u0131r, daha sonra her par\u00e7ay\u0131 ayr\u0131 ayr\u0131 modele g\u00f6nderir ve son olarak \u00f6zetlenmi\u015f par\u00e7alar\u0131 birle\u015ftirerek tam bir \u00f6zeti olu\u015fturur. LangChain'in mod\u00fcler yap\u0131s\u0131 sayesinde, bu bile\u015fenleri istedi\u011finiz \u015fekilde birle\u015ftirebilir ve kendi \u00f6zel i\u015f ak\u0131\u015flar\u0131n\u0131z\u0131 olu\u015fturabilirsiniz. Bu esneklik, \u00e7e\u015fitli uygulamalar\u0131 desteklemek i\u00e7in olduk\u00e7a \u00f6nemlidir.<\/p>\n<h2>\u0130lk LangChain Uygulaman\u0131z: Basit Bir Chatbot<\/h2>\n<p>\u015eimdi, LangChain kullanarak basit bir chatbot olu\u015ftural\u0131m. Bu \u00f6rnekte, OpenAI'nin GPT-3.5-turbo modelini kullanaca\u011f\u0131z. \u00d6ncelikle gerekli k\u00fct\u00fcphaneleri kurman\u0131z gerekmektedir:<\/p>\n<p> <code><br \/>\n pip install langchain openai<br \/>\n <\/code><\/p>\n<p>Ard\u0131ndan, a\u015fa\u011f\u0131daki kodu \u00e7al\u0131\u015ft\u0131rabilirsiniz:<\/p>\n<pre>\n <code>\n from langchain.llms import OpenAI\n from langchain.prompts import PromptTemplate\n from langchain.chains import LLMChain\n\n # OpenAI API anahtar\u0131n\u0131z\u0131 burada belirtin\n import os\n os.environ[\"OPENAI_API_KEY\"] = \"YOUR_API_KEY\"\n\n llm = OpenAI(temperature=0)\n prompt_template = \"Soru: {question}\"\n prompt = PromptTemplate(input_variables=[\"question\"], template=prompt_template)\n chain = LLMChain(llm=llm, prompt=prompt)\n\n while True:\n user_input = input(\"Sorunuzu girin (\u00e7\u0131kmak i\u00e7in '\u00e7\u0131k\u0131\u015f' yaz\u0131n): \")\n if user_input.lower() == \"\u00e7\u0131k\u0131\u015f\":\n break\n response = chain.run(user_input)\n print(response)\n <\/code>\n <\/pre>\n<p>Bu kod, kullan\u0131c\u0131n\u0131n sorular\u0131n\u0131 OpenAI'nin GPT-3.5-turbo modeline iletmekte ve ald\u0131\u011f\u0131 cevab\u0131 ekranda g\u00f6stermektedir. “YOUR_API_KEY” yerine kendi OpenAI API anahtar\u0131n\u0131z\u0131 girmeyi unutmay\u0131n. Bu basit \u00f6rnek, LangChain'in ne kadar kolay kullan\u0131labilece\u011fini g\u00f6stermektedir. Daha geli\u015fmi\u015f chatbotlar i\u00e7in, daha karma\u015f\u0131k prompt'lar ve zincirlemeler kullanabilirsiniz.<\/p>\n<h2>Daha Geli\u015fmi\u015f LangChain Uygulamalar\u0131: Ger\u00e7ek D\u00fcnya Senaryolar\u0131<\/h2>\n<p>LangChain, basit chatbotlardan \u00e7ok daha karma\u015f\u0131k uygulamalar olu\u015fturman\u0131za imkan tan\u0131r. \u0130\u015fte baz\u0131 ger\u00e7ek d\u00fcnya senaryolar\u0131:<\/p>\n<h3>1. Belge \u00d6zetleme:<\/h3>\n<p>LangChain, b\u00fcy\u00fck belgeleri otomatik olarak \u00f6zetlemenizi sa\u011flar. \u00d6rne\u011fin, bir PDF dosyas\u0131n\u0131 y\u00fckleyerek, LangChain'i kullanarak ana noktalar\u0131 \u00f6zetleyebilirsiniz. Bu, ara\u015ft\u0131rma, bilgi toplama ve analiz gibi bir\u00e7ok alanda zaman kazand\u0131r\u0131r.<\/p>\n<h3>2. Veri Taban\u0131 Sorgular\u0131:<\/h3>\n<p>LangChain, do\u011fal dil sorgulamalar\u0131 kullanarak veri tabanlar\u0131na eri\u015fmenizi sa\u011flar. Kullan\u0131c\u0131lar\u0131n karma\u015f\u0131k SQL sorgular\u0131 yazmas\u0131na gerek kalmadan, do\u011fal dil ile veri taban\u0131ndan bilgi almalar\u0131n\u0131 m\u00fcmk\u00fcn k\u0131lar. Bu, veri analizi ve raporlama i\u015flemlerini kolayla\u015ft\u0131r\u0131r.<\/p>\n<h3>3. \u00c7oklu Dil Deste\u011fi:<\/h3>\n<p>LangChain, farkl\u0131 dilleri destekleyen LLM'lerle \u00e7al\u0131\u015fabilir. Bu \u00f6zellik, uluslararas\u0131 pazarlara ula\u015fmak ve farkl\u0131 dil gruplar\u0131na hizmet vermek isteyen uygulamalar i\u00e7in olduk\u00e7a \u00f6nemlidir. \u00c7eviri i\u015flemleri ve \u00e7ok dilli chatbot geli\u015ftirme gibi alanlarda kullan\u0131labilir.<\/p>\n<h2>LangChain ile Performans\u0131 Optimize Etme: \u0130pu\u00e7lar\u0131 ve P\u00fcf Noktalar\u0131<\/h2>\n<p>LangChain ile uygulamalar\u0131n\u0131z\u0131 geli\u015ftirirken performans\u0131 optimize etmek i\u00e7in baz\u0131 \u00f6nemli ipu\u00e7lar\u0131 \u015funlard\u0131r:<\/p>\n<ul>\n<li><b>Uygun Prompts'lar Tasarlay\u0131n:<\/b> Net ve \u00f6zl\u00fc prompt'lar kullanarak LLM'lerin daha do\u011fru ve verimli yan\u0131tlar \u00fcretmesini sa\u011flayabilirsiniz.<\/li>\n<li><b>Cachelemeyi Kullan\u0131n:<\/b> Daha \u00f6nce cevaplanm\u0131\u015f olan sorular\u0131n \u00f6nbelle\u011fe al\u0131nmas\u0131, tekrarlanan istekleri azaltarak performans\u0131 art\u0131r\u0131r.<\/li>\n<li><b>Paralelle\u015ftirmeyi Uygulay\u0131n:<\/b> LangChain'in paralel i\u015fleme yeteneklerini kullanarak, i\u015flemlerinizi h\u0131zland\u0131rabilirsiniz. \u00d6zellikle b\u00fcy\u00fck veri setleri ile \u00e7al\u0131\u015f\u0131rken paralellik \u00f6nemli bir performans iyile\u015ftirmesi sa\u011flar.<\/li>\n<li><b>Veri \u00d6n \u0130\u015fleme:<\/b> Verilerinizi \u00f6nceden i\u015flemek, LLM'lerin daha h\u0131zl\u0131 \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flar. \u00d6rne\u011fin, b\u00fcy\u00fck metinleri par\u00e7alara ay\u0131rmak veya veri temizleme i\u015flemleri yaparak performans\u0131 art\u0131rabilirsiniz.<\/li>\n<\/ul>\n<h2>\u00d6\u011frenme Yol Haritas\u0131<\/h2>\n<h3>Yeni Ba\u015flayanlar \u0130\u00e7in:<\/h3>\n<p>LangChain'e giri\u015f yaparken, temel kavramlar\u0131 \u00f6\u011frenerek ba\u015flay\u0131n. Basit bir chatbot \u00f6rne\u011fiyle pratik yap\u0131n ve farkl\u0131 LLM'leri deneyin. Daha sonra, LangChain dok\u00fcmantasyonunu inceleyerek daha fazla bilgi edinebilirsiniz.<\/p>\n<h3>Orta Seviye:<\/h3>\n<p>Daha geli\u015fmi\u015f uygulamalar geli\u015ftirmeye odaklan\u0131n. Belge \u00f6zetleme, veri taban\u0131 sorgulama gibi ger\u00e7ek d\u00fcnya senaryolar\u0131n\u0131 deneyin. Farkl\u0131 zincirleme (chains) tiplerini ke\u015ffederek uygulamalar\u0131n\u0131z\u0131 daha da geli\u015ftirin.<\/p>\n<h3>\u0130leri Seviye:<\/h3>\n<p>Performans optimizasyonuna odaklan\u0131n. Paralelle\u015ftirme tekniklerini uygulay\u0131n ve kendi \u00f6zel zincirlemelerinizi olu\u015fturun. LangChain'i farkl\u0131 veri kaynaklar\u0131yla entegre edin ve karma\u015f\u0131k uygulamalar geli\u015ftirin. Ayr\u0131ca, LangChain'in geli\u015fmi\u015f \u00f6zelliklerini (\u00f6rne\u011fin, agent'ler) ara\u015ft\u0131r\u0131n.<\/p>\n<h2>Sonu\u00e7<\/h2>\n<p>LangChain, b\u00fcy\u00fck dil modellerini uygulamalar\u0131n\u0131za entegre etmenizi kolayla\u015ft\u0131ran g\u00fc\u00e7l\u00fc bir ara\u00e7t\u0131r. Bu makalede, LangChain'in temel kavramlar\u0131n\u0131, kullan\u0131m\u0131n\u0131 ve ileri tekniklerini \u00f6\u011frendiniz. Ger\u00e7ek d\u00fcnya \u00f6rnekleri ve ipu\u00e7lar\u0131yla, kendi LLM tabanl\u0131 uygulamalar\u0131n\u0131z\u0131 geli\u015ftirmeye ba\u015flayabilirsiniz. Daha fazla bilgi ve \u00f6rnekler i\u00e7in LangChain'in resmi dok\u00fcmantasyonuna ve <a href=\"https:\/\/fatihsoysal.com\">fatihsoysal.com<\/a> adresine g\u00f6z atabilirsiniz.<\/p>\n<h2>S\u0131k\u00e7a Sorulan Sorular<\/h2>\n<ul>\n<li><b>LangChain hangi programlama dillerini destekler?<\/b> LangChain, \u015fu anda \u00f6ncelikle Python'\u0131 desteklemektedir.<\/li>\n<li><b>LangChain hangi LLM'leri destekler?<\/b> LangChain, OpenAI, Google AI, Hugging Face gibi bir\u00e7ok LLM sa\u011flay\u0131c\u0131s\u0131yla uyumludur.<\/li>\n<li><b>LangChain'in \u00f6\u011frenme e\u011frisi nas\u0131l?<\/b> LangChain'in \u00f6\u011frenme e\u011frisi, temel kavramlar\u0131 kavrad\u0131ktan sonra nispeten kolayd\u0131r. \u0130yi haz\u0131rlanm\u0131\u015f dok\u00fcmantasyon ve \u00f6rnekler mevcuttur.<\/li>\n<li><b>LangChain \u00fccretsiz mi?<\/b> LangChain a\u00e7\u0131k kaynakl\u0131 bir projedir ve \u00fccretsiz olarak kullan\u0131labilir. Ancak, kulland\u0131\u011f\u0131n\u0131z LLM'lerin \u00fccretlendirmesi olabilir.<\/li>\n<li><b>LangChain g\u00fcvenli mi?<\/b>\n<div class=\"warning\"><b>Dikkat:<\/b> LangChain'i kullan\u0131rken, kullan\u0131c\u0131lardan al\u0131nan verilerin g\u00fcvenli\u011fini sa\u011flamak ve zararl\u0131 i\u00e7eriklerin engellenmesi i\u00e7in gerekli \u00f6nlemleri alman\u0131z \u00f6nemlidir. Uygulaman\u0131z\u0131n g\u00fcvenlik a\u00e7\u0131klar\u0131n\u0131 d\u00fczenli olarak kontrol etmelisiniz. <\/div>\n<\/li>\n<\/ul>\n<p>Yazar: Fatih Soysal<\/p>\n<p><\/body><br \/>\n<\/html><\/code><\/p>\n","protected":false},"excerpt":{"rendered":"LangChain: \u0130lk LLM Uygulaman\u0131z\u0131 Geli\u015ftirme LangChain: \u0130lk LLM Uygulaman\u0131z\u0131 Geli\u015ftirme B\u00fcy\u00fck dil modelleri (LLM'ler) art\u0131k hayat\u0131m\u0131z\u0131n her alan\u0131nda…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-29155","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n<title>LangChain: \u0130lk LLM Uygulaman\u0131z\u0131 Geli\u015ftirme<\/title>\n<meta name=\"description\" content=\"B\u00fcy\u00fck dil modelleri (LLM'ler) art\u0131k hayat\u0131m\u0131z\u0131n her alan\u0131nda yer al\u0131yor. Ancak bu g\u00fc\u00e7l\u00fc ara\u00e7lar\u0131 etkin bir \u015fekilde kullanmak i\u00e7in do\u011fru ara\u00e7lara ve bilgiye sahip olmak gerekiyor. \u0130\u015fte burada LangChain devreye giriyor. Bu makalede, LangChain'i kullanarak kendi LLM tabanl\u0131 uygulaman\u0131z\u0131 nas\u0131l olu\u015fturaca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz. Temel kavramlardan ba\u015flayarak ileri tekniklere kadar, ger\u00e7ek d\u00fcnya \u00f6rnekleriyle desteklenmi\u015f kapsaml\u0131 bir yol haritas\u0131 sunaca\u011f\u0131z.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/langchain-ilk-llm-uygulamanizi-gelistirme\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LangChain: \u0130lk LLM Uygulaman\u0131z\u0131 Geli\u015ftirme\" \/>\n<meta property=\"og:description\" content=\"B\u00fcy\u00fck dil modelleri (LLM'ler) art\u0131k hayat\u0131m\u0131z\u0131n her alan\u0131nda yer al\u0131yor. Ancak bu g\u00fc\u00e7l\u00fc ara\u00e7lar\u0131 etkin bir \u015fekilde kullanmak i\u00e7in do\u011fru ara\u00e7lara ve bilgiye sahip olmak gerekiyor. \u0130\u015fte burada LangChain devreye giriyor. Bu makalede, LangChain'i kullanarak kendi LLM tabanl\u0131 uygulaman\u0131z\u0131 nas\u0131l olu\u015fturaca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz. Temel kavramlardan ba\u015flayarak ileri tekniklere kadar, ger\u00e7ek d\u00fcnya \u00f6rnekleriyle desteklenmi\u015f kapsaml\u0131 bir yol haritas\u0131 sunaca\u011f\u0131z.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/langchain-ilk-llm-uygulamanizi-gelistirme\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2025-09-08T10:30:24+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/langchain-ilk-llm-uygulamanizi-gelistirme\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/langchain-ilk-llm-uygulamanizi-gelistirme\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"LangChain: \u0130lk LLM Uygulaman\u0131z\u0131 Geli\u015ftirme\",\"datePublished\":\"2025-09-08T10:30:24+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/langchain-ilk-llm-uygulamanizi-gelistirme\/\"},\"wordCount\":1228,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/langchain-ilk-llm-uygulamanizi-gelistirme\/#respond\"]}],\"copyrightYear\":\"2025\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/langchain-ilk-llm-uygulamanizi-gelistirme\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/langchain-ilk-llm-uygulamanizi-gelistirme\/\",\"name\":\"LangChain: \u0130lk LLM Uygulaman\u0131z\u0131 Geli\u015ftirme\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2025-09-08T10:30:24+00:00\",\"description\":\"B\u00fcy\u00fck dil modelleri (LLM'ler) art\u0131k hayat\u0131m\u0131z\u0131n her alan\u0131nda yer al\u0131yor. 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