B\u00fcy\u00fck dil modelleri (LLM’ler) tabanl\u0131 uygulamalar\u0131 geli\u015ftirirken kar\u015f\u0131la\u015f\u0131lan en b\u00fcy\u00fck zorluklardan biri, karma\u015f\u0131k i\u015f ak\u0131\u015flar\u0131n\u0131 \u00fcretim ortam\u0131na g\u00fcvenilir ve \u00f6l\u00e7eklenebilir bir \u015fekilde da\u011f\u0131tmakt\u0131r. Bu makale, LangGraph.js ile olu\u015fturulan dinamik ve reaktif LLM i\u015f ak\u0131\u015flar\u0131n\u0131 Open LangGraph Server kullanarak nas\u0131l sorunsuz bir \u015fekilde \u00fcretime alaca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m a\u00e7\u0131kl\u0131yor, geli\u015ftirme s\u00fcre\u00e7lerinizi kolayla\u015ft\u0131r\u0131yor ve uygulaman\u0131z\u0131n performans\u0131n\u0131 art\u0131rman\u0131n yollar\u0131n\u0131 g\u00f6steriyor.<\/p>\n
Modern yapay zeka (AI) uygulamalar\u0131, \u00f6zellikle b\u00fcy\u00fck dil modelleri etraf\u0131nda \u015fekillenenler, genellikle birden fazla ad\u0131m\u0131, ko\u015fullu dallanmalar\u0131 ve kullan\u0131c\u0131 etkile\u015fimlerini i\u00e7eren karma\u015f\u0131k i\u015f ak\u0131\u015flar\u0131na sahiptir. Geleneksel uygulama geli\u015ftirme y\u00f6ntemleri, bu t\u00fcr dinamik ve adaptif sistemlerin y\u00f6netiminde yetersiz kalabilir. \u0130\u015fte tam bu noktada LangGraph.js ve Open LangGraph Server (OLS) devreye girer, geli\u015ftiricilere LLM tabanl\u0131 uygulamalar\u0131 \u00f6l\u00e7eklenebilir, s\u00fcrd\u00fcr\u00fclebilir ve esnek bir \u015fekilde tasarlama, da\u011f\u0131tma ve y\u00f6netme imkan\u0131 sunar.<\/p>\n
LangGraph.js, ad\u0131ndan da anla\u015f\u0131laca\u011f\u0131 gibi LangChain ekosisteminin bir par\u00e7as\u0131d\u0131r ve geli\u015ftiricilerin durum bilgisi olan (stateful) \u00e7ok a\u015famal\u0131 LLM uygulamalar\u0131n\u0131, \u00f6zellikle ajanlar\u0131 (agents) ve \u00e7ok akt\u00f6rl\u00fc sistemleri olu\u015fturmas\u0131na olanak tan\u0131r. Bir graf teorisi prensibiyle \u00e7al\u0131\u015fan LangGraph, her ad\u0131m\u0131 bir d\u00fc\u011f\u00fcm (node), ad\u0131mlar aras\u0131ndaki ge\u00e7i\u015fleri ise kenar (edge) olarak tan\u0131mlayarak uygulaman\u0131n ak\u0131\u015f\u0131n\u0131 g\u00f6rselle\u015ftirmeyi ve y\u00f6netmeyi basitle\u015ftirir. Bu yap\u0131 sayesinde, bir kullan\u0131c\u0131 girdisine ba\u011fl\u0131 olarak farkl\u0131 yollar izleyebilen, \u00f6nceki etkile\u015fimleri hat\u0131rlayan ve karma\u015f\u0131k karar verme s\u00fcre\u00e7lerini y\u00fcr\u00fctebilen ak\u0131ll\u0131 sistemler kolayca in\u015fa edilebilir. \u00d6rne\u011fin, bir chatbotun kullan\u0131c\u0131n\u0131n sorusuna g\u00f6re bir bilgi taban\u0131nda arama yapmas\u0131, ard\u0131ndan bir API \u00e7a\u011f\u0131rmas\u0131 ve son olarak elde edilen bilgiyi \u00f6zetleyerek sunmas\u0131 gibi ad\u0131mlar, LangGraph ile \u015feffaf bir \u015fekilde modellenir.<\/p>\n
Ancak, harika bir i\u015f ak\u0131\u015f\u0131 olu\u015fturmak sadece ba\u015flang\u0131\u00e7t\u0131r. \u00dcretim ortam\u0131na da\u011f\u0131t\u0131m, s\u00fcr\u00fcm kontrol\u00fc, performans izleme, \u00f6l\u00e7eklenebilirlik ve g\u00fcvenlik gibi zorluklar geli\u015ftiricilerin kar\u015f\u0131s\u0131na \u00e7\u0131kar. Open LangGraph Server (OLS) bu zorluklar\u0131 a\u015fmak i\u00e7in tasarlanm\u0131\u015ft\u0131r. OLS, LangGraph i\u015f ak\u0131\u015flar\u0131n\u0131z\u0131 bir API hizmeti olarak sunman\u0131z\u0131 sa\u011flayan a\u00e7\u0131k kaynakl\u0131 bir sunucu \u00e7\u00f6z\u00fcm\u00fcd\u00fcr. Bu sayede, geli\u015ftirilen i\u015f ak\u0131\u015flar\u0131 do\u011frudan web uygulamalar\u0131na, mobil uygulamalara veya di\u011fer backend servislerine entegre edilebilir. OLS, tek ba\u015f\u0131na bir monolitik uygulama yerine mikroservis mimarisine uygun bir yakla\u015f\u0131m sunar; bu da her bir i\u015f ak\u0131\u015f\u0131n\u0131n ba\u011f\u0131ms\u0131z olarak da\u011f\u0131t\u0131lmas\u0131na, \u00f6l\u00e7eklenmesine ve g\u00fcncellenmesine olanak tan\u0131r. Ayr\u0131ca, yerle\u015fik izleme ve g\u00fcnl\u00fckleme yetenekleri sayesinde \u00fcretimdeki i\u015f ak\u0131\u015flar\u0131n\u0131z\u0131n performans\u0131n\u0131 kolayca takip edebilir, potansiyel sorunlar\u0131 proaktif bir \u015fekilde tespit edebilirsiniz. G\u00fcvenlik, s\u00fcr\u00fcmleme ve da\u011f\u0131t\u0131m otomasyonu (CI\/CD) i\u00e7in sundu\u011fu ara\u00e7lar, AI tabanl\u0131 uygulamalar\u0131n ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc basitle\u015ftirir. K\u0131sacas\u0131, LangGraph.js g\u00fc\u00e7l\u00fc i\u015f ak\u0131\u015flar\u0131 olu\u015fturman\u0131z\u0131 sa\u011flarken, Open LangGraph Server bu i\u015f ak\u0131\u015flar\u0131n\u0131 g\u00fcvenilir ve verimli bir \u015fekilde \u00fcretim ortam\u0131nda \u00e7al\u0131\u015ft\u0131rman\u0131z i\u00e7in gerekli altyap\u0131y\u0131 sunar. Bu ikili, LLM destekli uygulamalar\u0131n geli\u015ftirme ve da\u011f\u0131t\u0131m s\u00fcre\u00e7lerini devrim niteli\u011finde basitle\u015ftirir.<\/p>\n
LangGraph.js, karma\u015f\u0131k LLM tabanl\u0131 uygulamalar\u0131 ad\u0131m ad\u0131m in\u015fa etmenizi sa\u011flayan g\u00fc\u00e7l\u00fc bir ara\u00e7t\u0131r. Bir i\u015f ak\u0131\u015f\u0131 olu\u015fturmak i\u00e7in temel olarak d\u00fc\u011f\u00fcmler (nodes) ve kenarlar (edges) tan\u0131mlaman\u0131z gerekir. Her d\u00fc\u011f\u00fcm, bir LLM \u00e7a\u011fr\u0131s\u0131, bir ara\u00e7 kullan\u0131m\u0131, bir veritaban\u0131 sorgusu veya basit bir veri i\u015fleme gibi belirli bir g\u00f6revi temsil eder. Kenarlar ise bu d\u00fc\u011f\u00fcmler aras\u0131ndaki ge\u00e7i\u015fleri ve ak\u0131\u015f\u0131 belirler.<\/p>\n
\u0130lk ad\u0131m olarak, projenizi kurmal\u0131 ve gerekli ba\u011f\u0131ml\u0131l\u0131klar\u0131 y\u00fcklemelisiniz. Bir Node.js projesi olu\u015fturup LangGraph.js paketini ekleyerek ba\u015flayabilirsiniz:<\/p>\n
\nnpm init -y\nnpm install @langchain\/langgraph langchain @langchain\/openai\n <\/pre>\n<\/code><\/p>\n
Bu komutlar, temel bir Node.js projesi ba\u015flat\u0131r ve LangGraph ile OpenAI entegrasyonu i\u00e7in gerekli paketleri y\u00fckler. Ard\u0131ndan, bir LangGraph i\u015f ak\u0131\u015f\u0131 tan\u0131mlamak i\u00e7in bir Graph<\/code> nesnesi olu\u015fturursunuz. \u0130\u015f ak\u0131\u015f\u0131n\u0131z\u0131n durumunu tutmak i\u00e7in bir State<\/code> aray\u00fcz\u00fc tan\u0131mlamak \u00f6nemlidir. Bu durum, d\u00fc\u011f\u00fcmler aras\u0131nda payla\u015f\u0131lan verileri i\u00e7erir.<\/p>\n\nimport { BaseMessage } from \"@langchain\/core\/messages\";\nimport { StateGraph } from \"@langchain\/langgraph\";\nimport { ChatOpenAI } from \"@langchain\/openai\";\n\n\/\/ Durum aray\u00fcz\u00fcm\u00fcz\u00fc tan\u0131ml\u0131yoruz\ninterface AgentState {\n messages: BaseMessage[];\n next: string; \/\/ Hangi d\u00fc\u011f\u00fcme gidilece\u011fini belirlemek i\u00e7in\n}\n\n\/\/ Bir \u00f6rnek d\u00fc\u011f\u00fcm fonksiyonu\nconst callModel = async (state: AgentState) => {\n const model = new ChatOpenAI({\n modelName: \"gpt-4o\",\n temperature: 0,\n });\n const response = await model.invoke(state.messages);\n return {\n messages: [...state.messages, response],\n next: \"tool_use_or_finish\", \/\/ Sonraki ad\u0131m\u0131 belirliyoruz\n };\n};\n\n\/\/ Ba\u015fka bir \u00f6rnek d\u00fc\u011f\u00fcm fonksiyonu\nconst toolNode = async (state: AgentState) => {\n \/\/ Burada bir ara\u00e7 (API, DB sorgusu vb.) \u00e7a\u011fr\u0131labilir\n console.log(\"Ara\u00e7 \u00e7a\u011fr\u0131l\u0131yor...\");\n const toolResponse = {\n type: \"tool_response\",\n content: \"Ara\u00e7tan gelen yan\u0131t: Hava durumu bilgisi.\",\n };\n return {\n messages: [...state.messages, toolResponse],\n next: \"finish\",\n };\n};\n\n\/\/ Graf\u0131m\u0131z\u0131 olu\u015fturuyoruz\nconst workflow = new StateGraph()\n .addNode(\"model\", callModel)\n .addNode(\"tool\", toolNode);\n\n\/\/ Ba\u015flang\u0131\u00e7 noktas\u0131n\u0131 belirliyoruz\nworkflow.setEntryPoint(\"model\");\n\n\/\/ Kenarlar\u0131 (ge\u00e7i\u015fleri) belirliyoruz\n\/\/ Ko\u015fullu kenarlar, bir d\u00fc\u011f\u00fcm\u00fcn \u00e7\u0131k\u0131\u015f\u0131na g\u00f6re farkl\u0131 yollara gitmeyi sa\u011flar\nworkflow.addConditionalEdges(\n \"model\",\n (state: AgentState) => state.next, \/\/ 'next' \u00f6zelli\u011fi karar\u0131 verir\n {\n tool_use_or_finish: \"tool\", \/\/ 'tool_use_or_finish' ise 'tool' d\u00fc\u011f\u00fcm\u00fcne git\n finish: \"model\", \/\/ 'finish' ise tekrar 'model' d\u00fc\u011f\u00fcm\u00fcne d\u00f6n (bu bir \u00f6rnek, ger\u00e7ekte sonland\u0131r\u0131labilir)\n }\n);\nworkflow.addEdg(\"tool\", \"model\"); \/\/ Tool'dan sonra tekrar modele d\u00f6n\n\n\/\/ \u00c7\u0131k\u0131\u015f noktas\u0131n\u0131 belirliyoruz (iste\u011fe ba\u011fl\u0131, genellikle d\u00f6ng\u00fcsel olabilir)\n\/\/ workflow.setFinishPoint(\"finish\"); \/\/ E\u011fer bir 'finish' d\u00fc\u011f\u00fcm\u00fc olsayd\u0131\n\nconst app = workflow.compile();\n <\/pre>\n<\/code><\/p>\n
Yukar\u0131daki \u00f6rnekte, basit bir sohbet ak\u0131\u015f\u0131 tasvir edilmi\u015ftir. callModel<\/code> d\u00fc\u011f\u00fcm\u00fc bir LLM'i \u00e7a\u011f\u0131r\u0131rken, toolNode<\/code> d\u00fc\u011f\u00fcm\u00fc harici bir ara\u00e7 kullan\u0131m\u0131n\u0131 simgeler. addConditionalEdges<\/code> metodu, bir d\u00fc\u011f\u00fcm\u00fcn \u00e7\u0131kt\u0131s\u0131na g\u00f6re (burada state.next<\/code> de\u011feri) i\u015f ak\u0131\u015f\u0131n\u0131n hangi yola devam edece\u011fini belirlemenizi sa\u011flar. Bu, dinamik ve adaptif LLM uygulamalar\u0131 i\u00e7in kritik bir \u00f6zelliktir. \u00d6rne\u011fin, model bir kullan\u0131c\u0131n\u0131n iste\u011finin bir ara\u00e7 kullan\u0131m\u0131n\u0131 gerektirdi\u011fine karar verirse, ak\u0131\u015f toolNode<\/code>'a y\u00f6nlendirilir. Aksi takdirde, ak\u0131\u015f ba\u015fka bir yola devam edebilir veya do\u011frudan sonland\u0131r\u0131labilir.<\/p>\nLangGraph.js ayr\u0131ca geri d\u00f6ng\u00fcler (loops) olu\u015fturman\u0131za da imkan tan\u0131r, bu da ajanlar\u0131n tekrar tekrar d\u00fc\u015f\u00fcnme ve eylem d\u00f6ng\u00fcleri ger\u00e7ekle\u015ftirmesi i\u00e7in hayati \u00f6neme sahiptir. Bu, kullan\u0131c\u0131 girdilerini daha derinlemesine analiz etmek, hatalar\u0131 d\u00fczeltmek veya karma\u015f\u0131k g\u00f6revleri ad\u0131m ad\u0131m tamamlamak i\u00e7in kullan\u0131labilir. \u0130\u015f ak\u0131\u015f\u0131n\u0131z\u0131 derledikten sonra (workflow.compile()<\/code>), bunu bir kez \u00e7al\u0131\u015ft\u0131rabilir veya Open LangGraph Server gibi bir platforma da\u011f\u0131tarak API arac\u0131l\u0131\u011f\u0131yla kullan\u0131ma sunabilirsiniz. Bu yap\u0131, LLM uygulamalar\u0131n\u0131z\u0131n sadece zekice de\u011fil, ayn\u0131 zamanda y\u00f6netilebilir ve \u00f6l\u00e7eklenebilir olmas\u0131n\u0131 sa\u011flar.<\/p>\nOpen LangGraph Server Nedir ve \u00dcretim Ortam\u0131nda Ne Gibi Avantajlar Sunar?<\/h2>\n
Open LangGraph Server (OLS), LangGraph.js ile olu\u015fturdu\u011funuz karma\u015f\u0131k LLM i\u015f ak\u0131\u015flar\u0131n\u0131 \u00fcretim ortam\u0131nda API olarak sunmak i\u00e7in tasarlanm\u0131\u015f, a\u00e7\u0131k kaynakl\u0131 ve robust bir altyap\u0131d\u0131r. Geli\u015ftiricilerin i\u015f ak\u0131\u015flar\u0131n\u0131 do\u011frudan sunucu \u00fczerinde \u00e7al\u0131\u015ft\u0131rmalar\u0131na, y\u00f6netmelerine ve izlemelerine olanak tan\u0131yarak, LLM tabanl\u0131 uygulamalar\u0131n da\u011f\u0131t\u0131m s\u00fcrecini basitle\u015ftirir ve h\u0131zland\u0131r\u0131r. OLS'nin temel amac\u0131, LangGraph'\u0131n sa\u011flad\u0131\u011f\u0131 g\u00fc\u00e7l\u00fc i\u015f ak\u0131\u015f\u0131 modelleme yeteneklerini, ger\u00e7ek d\u00fcnya \u00fcretim gereksinimleriyle birle\u015ftirmektir.<\/p>\n
OLS, bir web sunucusu olarak \u00e7al\u0131\u015f\u0131r ve LangGraph i\u015f ak\u0131\u015flar\u0131n\u0131z\u0131 RESTful API endpoint'leri arac\u0131l\u0131\u011f\u0131yla d\u0131\u015f d\u00fcnyaya a\u00e7ar. Bu, herhangi bir programlama dilinde yaz\u0131lm\u0131\u015f frontend veya backend uygulamas\u0131n\u0131n, sadece HTTP \u00e7a\u011fr\u0131lar\u0131 yaparak LLM i\u015f ak\u0131\u015flar\u0131n\u0131zla etkile\u015fime girmesini m\u00fcmk\u00fcn k\u0131lar. \u00d6rne\u011fin, bir mobil uygulama, kullan\u0131c\u0131dan gelen sorguyu OLS'ye g\u00f6ndererek, sunucu taraf\u0131nda \u00e7al\u0131\u015fan LangGraph i\u015f ak\u0131\u015f\u0131n\u0131n bu sorguyu i\u015flemesini ve yan\u0131t \u00fcretmesini sa\u011flayabilir. Bu mimari, frontend ve LLM mant\u0131\u011f\u0131 aras\u0131nda temiz bir ayr\u0131m yarat\u0131r, her iki taraf\u0131n da ba\u011f\u0131ms\u0131z olarak geli\u015ftirilmesine ve \u00f6l\u00e7eklenmesine olanak tan\u0131r.<\/p>\n
\u00dcretim ortam\u0131nda OLS'nin sundu\u011fu avantajlar saymakla bitmez:<\/p>\n
\n- \u00d6l\u00e7eklenebilirlik:<\/strong> OLS, i\u015f ak\u0131\u015flar\u0131n\u0131z\u0131n yo\u011funlu\u011funu y\u00f6netmek i\u00e7in \u00f6l\u00e7eklenebilir bir yap\u0131ya sahiptir. Kubernetes gibi konteyner orkestrasyon ara\u00e7lar\u0131yla kolayca entegre edilebilir, b\u00f6ylece talebe g\u00f6re otomatik olarak \u00f6l\u00e7eklenir ve y\u00fck dengelemesi yapar. Bu, ani trafik art\u0131\u015flar\u0131na kar\u015f\u0131 uygulaman\u0131z\u0131n dayan\u0131kl\u0131l\u0131\u011f\u0131n\u0131 art\u0131r\u0131r.<\/li>\n
- S\u00fcr\u00fcmleme ve A\/B Testi:<\/strong> OLS, i\u015f ak\u0131\u015flar\u0131n\u0131z\u0131n farkl\u0131 s\u00fcr\u00fcmlerini e\u015f zamanl\u0131 olarak da\u011f\u0131tman\u0131za olanak tan\u0131r. Bu sayede, yeni i\u015f ak\u0131\u015f\u0131 versiyonlar\u0131n\u0131 g\u00fcvenli bir \u015fekilde devreye alabilir, A\/B testleri yaparak performanslar\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131rabilir ve en iyi performans\u0131 g\u00f6steren versiyonu t\u00fcm kullan\u0131c\u0131lara yayabilirsiniz. Bu, s\u00fcrekli iyile\u015ftirme ve yenilik\u00e7i \u00f6zelliklerin h\u0131zl\u0131 bir \u015fekilde kullan\u0131ma sunulmas\u0131 i\u00e7in kritik \u00f6neme sahiptir.<\/li>\n
- \u0130zleme ve G\u00f6zlemlenebilirlik:<\/strong> OLS, entegre g\u00fcnl\u00fckleme (logging) ve izleme (tracing) yetenekleri sunar. Her bir i\u015f ak\u0131\u015f\u0131 \u00e7al\u0131\u015ft\u0131rmas\u0131n\u0131n detaylar\u0131n\u0131 kaydedebilir, performans metriklerini toplayabilir ve potansiyel darbo\u011fazlar\u0131 veya hatalar\u0131 h\u0131zla tespit edebilirsiniz. Prometheus, Grafana gibi pop\u00fcler izleme ara\u00e7lar\u0131yla entegrasyonu sayesinde, operasyonel g\u00f6r\u00fcn\u00fcrl\u00fc\u011f\u00fcn\u00fcz\u00fc art\u0131r\u0131rs\u0131n\u0131z.<\/li>\n
- G\u00fcvenlik:<\/strong> \u00dcretim ortam\u0131nda \u00e7al\u0131\u015fan API'ler i\u00e7in g\u00fcvenlik hayati \u00f6neme sahiptir. OLS, API anahtarlar\u0131, JWT tabanl\u0131 kimlik do\u011frulama ve yetkilendirme mekanizmalar\u0131 sunarak i\u015f ak\u0131\u015flar\u0131n\u0131za eri\u015fimi k\u0131s\u0131tlaman\u0131za ve g\u00fcvence alt\u0131na alman\u0131za yard\u0131mc\u0131 olur. Bu, hassas verilerin ve LLM \u00e7a\u011fr\u0131lar\u0131n\u0131n k\u00f6t\u00fc niyetli eri\u015fimden korunmas\u0131n\u0131 sa\u011flar.<\/li>\n
- Basit Da\u011f\u0131t\u0131m ve Y\u00f6netim:<\/strong> OLS CLI veya API'si arac\u0131l\u0131\u011f\u0131yla LangGraph i\u015f ak\u0131\u015flar\u0131n\u0131z\u0131 kolayca paketleyebilir ve da\u011f\u0131tabilirsiniz. Bu, CI\/CD s\u00fcre\u00e7lerinize entegrasyonu basitle\u015ftirir ve i\u015f ak\u0131\u015f\u0131 g\u00fcncellemelerini otomatikle\u015ftirebilirsiniz. Geli\u015ftiricilerin, altyap\u0131 y\u00f6netimi yerine do\u011frudan i\u015f mant\u0131\u011f\u0131na odaklanmas\u0131na olanak tan\u0131r.<\/li>\n
- Dil Ba\u011f\u0131ms\u0131zl\u0131\u011f\u0131:<\/strong> OLS bir API hizmeti oldu\u011fundan, LangGraph i\u015f ak\u0131\u015flar\u0131n\u0131z herhangi bir programlama dilinden eri\u015filebilir hale gelir. Python, Java, Go, Ruby veya C# gibi dillerde yaz\u0131lm\u0131\u015f uygulamalar, HTTP \u00e7a\u011fr\u0131lar\u0131 arac\u0131l\u0131\u011f\u0131yla LangGraph i\u015f ak\u0131\u015flar\u0131n\u0131zdan faydalanabilir.<\/li>\n<\/ul>\n
\u00d6zetle, Open LangGraph Server, LangGraph.js ile olu\u015fturdu\u011funuz ak\u0131ll\u0131 uygulamalar\u0131n sadece prototip a\u015famas\u0131nda kalmamas\u0131n\u0131, ayn\u0131 zamanda ger\u00e7ek d\u00fcnya kullan\u0131m senaryolar\u0131nda, y\u00fcksek performansl\u0131 ve g\u00fcvenilir bir \u015fekilde \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flayan kritik bir k\u00f6pr\u00fc g\u00f6revi g\u00f6r\u00fcr. Bu, AI destekli \u00fcr\u00fcnlerin pazara s\u00fcr\u00fclme s\u00fcresini k\u0131salt\u0131rken, operasyonel y\u00fck\u00fc de \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131r.<\/p>\n
LangGraph.js \u0130\u015f Ak\u0131\u015f\u0131n\u0131z\u0131 Open LangGraph Server'a Nas\u0131l Da\u011f\u0131t\u0131rs\u0131n\u0131z?<\/h3>\n
LangGraph.js ile harika bir i\u015f ak\u0131\u015f\u0131 geli\u015ftirdikten sonra, bunu Open LangGraph Server (OLS) arac\u0131l\u0131\u011f\u0131yla \u00fcretim ortam\u0131na da\u011f\u0131tmak, uygulaman\u0131z\u0131n potansiyelini tam anlam\u0131yla ortaya \u00e7\u0131kar\u0131r. Da\u011f\u0131t\u0131m s\u00fcreci, i\u015f ak\u0131\u015f\u0131n\u0131z\u0131 bir OLS uyumlu pakete d\u00f6n\u00fc\u015ft\u00fcrmeyi, ard\u0131ndan bu paketi sunucuya y\u00fcklemeyi ve API olarak kullan\u0131ma sunmay\u0131 i\u00e7erir. \u0130\u015fte ad\u0131m ad\u0131m nas\u0131l yap\u0131laca\u011f\u0131:<\/p>\n
1. \u0130\u015f Ak\u0131\u015f\u0131n\u0131z\u0131 OLS \u0130\u00e7in Haz\u0131rlama ve Paketleme<\/h4>\n
LangGraph.js i\u015f ak\u0131\u015f\u0131n\u0131z, OLS taraf\u0131ndan t\u00fcketilebilecek bir formatta olmal\u0131d\u0131r. Genellikle bu, i\u015f ak\u0131\u015f\u0131n\u0131z\u0131 d\u0131\u015fa aktaran bir JavaScript\/TypeScript mod\u00fcl\u00fc anlam\u0131na gelir. \u00d6nceki \u00f6rnekteki app<\/code> de\u011fi\u015fkeninizi bir dosyadan d\u0131\u015fa aktarman\u0131z gerekecektir:<\/p>\n\n\/\/ graph.ts veya graph.js\nimport { BaseMessage } from \"@langchain\/core\/messages\";\nimport { StateGraph } from \"@langchain\/langgraph\";\nimport { ChatOpenAI } from \"@langchain\/openai\";\n\n\/\/ Durum aray\u00fcz\u00fcm\u00fcz\u00fc tan\u0131ml\u0131yoruz\ninterface AgentState {\n messages: BaseMessage[];\n next: string;\n}\n\n\/\/ Bir \u00f6rnek d\u00fc\u011f\u00fcm fonksiyonu\nconst callModel = async (state: AgentState) => {\n const model = new ChatOpenAI({\n modelName: process.env.OPENAI_MODEL_NAME || \"gpt-4o\", \/\/ \u00c7evre de\u011fi\u015fkeni kullan\u0131m\u0131\n temperature: parseFloat(process.env.TEMPERATURE || \"0\"),\n openAIApiKey: process.env.OPENAI_API_KEY, \/\/ API anahtar\u0131n\u0131 g\u00fcvende tutun\n });\n const response = await model.invoke(state.messages);\n return {\n messages: [...state.messages, response],\n next: \"tool_use_or_finish\",\n };\n};\n\nconst toolNode = async (state: AgentState) => {\n console.log(\"Ara\u00e7 \u00e7a\u011fr\u0131l\u0131yor...\");\n const toolResponse = {\n type: \"tool_response\",\n content: \"Ara\u00e7tan gelen yan\u0131t: Hava durumu bilgisi.\",\n };\n return {\n messages: [...state.messages, toolResponse],\n next: \"finish\",\n };\n};\n\nconst workflow = new StateGraph()\n .addNode(\"model\", callModel)\n .addNode(\"tool\", toolNode);\n\nworkflow.setEntryPoint(\"model\");\nworkflow.addConditionalEdges(\n \"model\",\n (state: AgentState) => state.next,\n {\n tool_use_or_finish: \"tool\",\n finish: \"model\",\n }\n);\nworkflow.addEdge(\"tool\", \"model\");\n\nexport const agentWorkflow = workflow.compile(); \/\/ \u0130\u015f ak\u0131\u015f\u0131m\u0131z\u0131 d\u0131\u015fa aktar\u0131yoruz\n <\/pre>\n<\/code><\/p>\n
\u00c7evre de\u011fi\u015fkenlerinin kullan\u0131m\u0131, API anahtarlar\u0131 gibi hassas bilgilerin kod taban\u0131nda sabit kodlanmas\u0131n\u0131 \u00f6nler ve \u00fcretim ortam\u0131nda g\u00fcvenli\u011fi art\u0131r\u0131r. Ayr\u0131ca, OLS genellikle i\u015f ak\u0131\u015f\u0131n\u0131z\u0131 bir ba\u011f\u0131ml\u0131l\u0131k olarak kurman\u0131z\u0131 bekler. Bu nedenle, projenizi bir NPM paketi olarak haz\u0131rlaman\u0131z gerekebilir. package.json<\/code> dosyan\u0131zda main<\/code> veya exports<\/code> alan\u0131n\u0131n i\u015f ak\u0131\u015f\u0131n\u0131z\u0131 d\u0131\u015fa aktard\u0131\u011f\u0131 dosyay\u0131 i\u015faret etti\u011finden emin olun.<\/p>\n2. Open LangGraph Server'\u0131 Kurma ve \u00c7al\u0131\u015ft\u0131rma<\/h4>\n
OLS'yi yerel makinenizde veya bir sunucuda \u00e7al\u0131\u015ft\u0131rabilirsiniz. Genellikle Docker konteynerleri arac\u0131l\u0131\u011f\u0131yla da\u011f\u0131t\u0131lmas\u0131 \u00f6nerilir, bu da kurulumu ve y\u00f6netimi basitle\u015ftirir:<\/p>\n
\ndocker run -p 8000:8000 -e OPENAI_API_KEY=\"your_api_key_here\" -e OPENAI_MODEL_NAME=\"gpt-4o\" your_ols_image_name\n <\/pre>\n<\/code><\/p>\n
your_ols_image_name<\/code> yerine OLS'nin resmi Docker imaj\u0131n\u0131 veya kendi olu\u015fturdu\u011funuz \u00f6zel imaj\u0131 kullanmal\u0131s\u0131n\u0131z. \u00c7evre de\u011fi\u015fkenleri (\u00f6rne\u011fin OPENAI_API_KEY<\/code>) i\u015f ak\u0131\u015f\u0131n\u0131zdaki LLM'ler i\u00e7in gerekli kimlik bilgilerini sa\u011flar.<\/p>\n3. \u0130\u015f Ak\u0131\u015f\u0131n\u0131z\u0131 OLS'ye Da\u011f\u0131tma<\/h4>\n
\u0130\u015f ak\u0131\u015f\u0131n\u0131z\u0131 OLS'ye da\u011f\u0131tman\u0131n birka\u00e7 yolu vard\u0131r. En yayg\u0131n olan\u0131, OLS'nin CLI arac\u0131n\u0131 veya API'sini kullanmakt\u0131r. Varsayal\u0131m ki i\u015f ak\u0131\u015f\u0131n\u0131z bir NPM paketi olarak mevcut ve my-agent-workflow<\/code> ad\u0131n\u0131 ta\u015f\u0131yor. OLS CLI ile da\u011f\u0131t\u0131m \u015fu \u015fekilde olabilir:<\/p>\n\n# OLS CLI'y\u0131 kurun (e\u011fer kurulu de\u011filse)\nnpm install -g @langgraph\/server-cli\n\n# \u0130\u015f ak\u0131\u015f\u0131n\u0131z\u0131 da\u011f\u0131t\u0131n\nols deploy my-agent-workflow --url http:\/\/localhost:8000 --name my-first-agent --version 1.0.0\n <\/pre>\n<\/code><\/p>\n
Bu komut, my-agent-workflow<\/code> paketini al\u0131r, OLS'ye y\u00fckler ve my-first-agent<\/code> ad\u0131yla 1.0.0<\/code> s\u00fcr\u00fcm\u00fc olarak kaydeder. OLS, bu i\u015f ak\u0131\u015f\u0131n\u0131 derler ve bir API endpoint'i olarak hizmete a\u00e7ar.<\/p>\n4. Da\u011f\u0131t\u0131lan \u0130\u015f Ak\u0131\u015f\u0131n\u0131 Kullanma<\/h4>\n
\u0130\u015f ak\u0131\u015f\u0131n\u0131z da\u011f\u0131t\u0131ld\u0131ktan sonra, bir HTTP POST iste\u011fi ile etkile\u015fime ge\u00e7ebilirsiniz. \u00d6rne\u011fin, bir curl komutu ile:<\/p>\n
\ncurl -X POST http:\/\/localhost:8000\/predict\/my-first-agent\/invoke \\\n -H \"Content-Type: application\/json\" \\\n -d '{\n \"input\": {\n \"messages\": [\n {\"type\": \"human\", \"content\": \"Merhaba! Hava durumu hakk\u0131nda bilgi alabilir miyim?\"}\n ]\n },\n \"config\": {\n \"configurable\": {\n \"thread_id\": \"user_123\"\n }\n }\n }'\n <\/pre>\n<\/code><\/p>\n
Bu \u00e7a\u011fr\u0131, i\u015f ak\u0131\u015f\u0131n\u0131z\u0131 ba\u015flat\u0131r, modelinizi ve ara\u00e7lar\u0131n\u0131z\u0131 \u00e7a\u011f\u0131r\u0131r ve yan\u0131t\u0131 geri d\u00f6nd\u00fcr\u00fcr. thread_id<\/code> gibi yap\u0131land\u0131r\u0131labilir parametreler, durum bilgisi olan LangGraph i\u015f ak\u0131\u015flar\u0131 i\u00e7in ge\u00e7mi\u015fi korumak amac\u0131yla kullan\u0131l\u0131r. OLS, her bir thread_id<\/code> i\u00e7in i\u015f ak\u0131\u015f\u0131n\u0131n durumunu otomatik olarak y\u00f6netir, b\u00f6ylece birden fazla kullan\u0131c\u0131 e\u015f zamanl\u0131 olarak etkile\u015fimde bulunabilir ve her biri kendi ge\u00e7mi\u015fine sahip olur.<\/p>\nBu da\u011f\u0131t\u0131m s\u00fcreci, LangGraph.js i\u015f ak\u0131\u015flar\u0131n\u0131z\u0131n karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 basitle\u015ftirir, geli\u015ftiricilere LLM uygulamalar\u0131n\u0131 h\u0131zl\u0131 ve g\u00fcvenilir bir \u015fekilde \u00fcretime alma yetene\u011fi kazand\u0131r\u0131r. Ayr\u0131ca, OLS'nin s\u00fcr\u00fcmleme yetenekleri sayesinde, yeni \u00f6zellikler eklemek veya mevcut i\u015f ak\u0131\u015flar\u0131n\u0131 optimize etmek art\u0131k \u00e7ok daha kolayd\u0131r.<\/p>\n
Ger\u00e7ek D\u00fcnya Senaryosu: Bir M\u00fc\u015fteri Destek Chatbotu \u00d6rne\u011fi<\/h2>\n
\u015eirketler i\u00e7in m\u00fc\u015fteri deste\u011fi, hem maliyetli hem de zaman al\u0131c\u0131 bir s\u00fcre\u00e7 olabilir. M\u00fc\u015fterilerin sorular\u0131n\u0131 h\u0131zl\u0131 ve etkili bir \u015fekilde yan\u0131tlamak, marka sadakati ve m\u00fc\u015fteri memnuniyeti a\u00e7\u0131s\u0131ndan hayati \u00f6neme sahiptir. \u0130\u015fte burada LangGraph.js ve Open LangGraph Server'\u0131n g\u00fcc\u00fcn\u00fc g\u00f6steren ger\u00e7ek bir d\u00fcnya senaryosu devreye giriyor: Ak\u0131ll\u0131 bir m\u00fc\u015fteri destek chatbotu olu\u015fturmak ve onu \u00fcretimde y\u00f6netmek.<\/p>\n
Bir e-ticaret \u015firketi d\u00fc\u015f\u00fcnelim. Bu \u015firketin, \u00fcr\u00fcn bilgileri, sipari\u015f durumu, iade politikalar\u0131 ve teknik destek gibi \u00e7e\u015fitli konularda m\u00fc\u015fterilere yard\u0131mc\u0131 olacak bir chatbota ihtiyac\u0131 var. Geleneksel chatbotlar genellikle kural tabanl\u0131d\u0131r ve karma\u015f\u0131k sorular\u0131 veya beklentileri kar\u015f\u0131lamakta zorlanabilir. LangGraph.js ile durum bilgisi olan ve dinamik bir chatbot tasarlayabiliriz.<\/p>\n
LangGraph.js ile M\u00fc\u015fteri Destek Chatbotunun Tasar\u0131m\u0131<\/h4>\n
Chatbotumuzun temel i\u015f ak\u0131\u015f\u0131 a\u015fa\u011f\u0131daki ad\u0131mlar\u0131 i\u00e7erecektir:<\/p>\n
\n- Giri\u015f (Human Input):<\/strong> M\u00fc\u015fteriden gelen mesaj\u0131 al\u0131r.<\/li>\n
- Intent Tan\u0131mlama (Intent Recognition):<\/strong> LLM kullanarak m\u00fc\u015fterinin amac\u0131n\u0131 (\u00fcr\u00fcn sorgulama, sipari\u015f takibi, iade iste\u011fi vb.) belirler. Bu, bir \"model\" d\u00fc\u011f\u00fcm\u00fc taraf\u0131ndan ger\u00e7ekle\u015ftirilir.<\/li>\n
- Bilgi Taban\u0131 Aramas\u0131 (Knowledge Base Search):<\/strong> E\u011fer intent bir bilgi sorgulamas\u0131ysa, \u015firket veritaban\u0131 veya SSS (S\u0131k\u00e7a Sorulan Sorular) dok\u00fcmanlar\u0131nda arama yapar. Bu, bir \"tool\" d\u00fc\u011f\u00fcm\u00fc olabilir.<\/li>\n
- Sipari\u015f Durumu Kontrol\u00fc (Order Status Check):<\/strong> E\u011fer intent sipari\u015f takibi ise, m\u00fc\u015fteri ID'si ve sipari\u015f numaras\u0131 ile \u015firket ERP\/CRM sistemine bir API \u00e7a\u011fr\u0131s\u0131 yapar. Bu da ba\u015fka bir \"tool\" d\u00fc\u011f\u00fcm\u00fcd\u00fcr.<\/li>\n
- Yan\u0131t \u00dcretme (Response Generation):<\/strong> Toplanan bilgilerle (bilgi taban\u0131 sonucu, sipari\u015f durumu vb.) birle\u015fen LLM, m\u00fc\u015fteriye do\u011fal dilde bir yan\u0131t olu\u015fturur. Bu, tekrar bir \"model\" d\u00fc\u011f\u00fcm\u00fcd\u00fcr.<\/li>\n
- Canl\u0131 Destek Y\u00f6nlendirme (Escalation to Live Agent):<\/strong> E\u011fer chatbot sorunu \u00e7\u00f6zemezse veya m\u00fc\u015fteri \u00f6zel bir durum ya\u015f\u0131yorsa, konu\u015fmay\u0131 canl\u0131 bir m\u00fc\u015fteri temsilcisine y\u00f6nlendirir. Bu, ko\u015fullu bir ge\u00e7i\u015f (conditional edge) ile tetiklenir.<\/li>\n
- D\u00f6ng\u00fc (Loop):<\/strong> Chatbot, sorunun tamamen \u00e7\u00f6z\u00fcl\u00fcp \u00e7\u00f6z\u00fclmedi\u011fini belirleyene kadar veya canl\u0131 deste\u011fe y\u00f6nlendirilene kadar bu ad\u0131mlar\u0131 tekrarlayabilir.<\/li>\n<\/ol>\n
Bu i\u015f ak\u0131\u015f\u0131, LangGraph'\u0131n d\u00fc\u011f\u00fcm ve kenar yap\u0131s\u0131 ile kolayca modellenir. Ko\u015fullu kenarlar, chatbotun belirlenen amaca g\u00f6re farkl\u0131 dallara ayr\u0131lmas\u0131n\u0131 sa\u011flar, \u00f6rne\u011fin bir arama arac\u0131na veya bir sipari\u015f API'sine y\u00f6nlendirme gibi. Durum y\u00f6netimi sayesinde, chatbot \u00f6nceki konu\u015fmalar\u0131 hat\u0131rlayabilir ve ba\u011flam\u0131 koruyabilir.<\/p>\n
Open LangGraph Server ile \u00dcretime Da\u011f\u0131t\u0131m ve Y\u00f6netim<\/h4>\n
Bu karma\u015f\u0131k LangGraph.js i\u015f ak\u0131\u015f\u0131n\u0131 olu\u015fturduktan sonra, Open LangGraph Server (OLS) devreye girer:<\/p>\n
\n- H\u0131zl\u0131 Da\u011f\u0131t\u0131m:<\/strong> Geli\u015ftirilen i\u015f ak\u0131\u015f\u0131, OLS CLI kullan\u0131larak saniyeler i\u00e7inde sunucuya da\u011f\u0131t\u0131l\u0131r ve bir API endpoint'i olarak hizmete a\u00e7\u0131l\u0131r. Bu, web siteleri, mobil uygulamalar veya i\u00e7 sistemler taraf\u0131ndan kolayca entegre edilebilir.<\/li>\n
- S\u00fcr\u00fcm Kontrol\u00fc ve A\/B Testi:<\/strong> \u015eirket, yeni bir LLM modeli veya daha iyi bir bilgi taban\u0131 arama arac\u0131 entegre etmek istedi\u011finde, chatbotun yeni bir s\u00fcr\u00fcm\u00fcn\u00fc olu\u015fturur. OLS sayesinde, bu yeni s\u00fcr\u00fcm mevcut \u00fcretim s\u00fcr\u00fcm\u00fcyle birlikte da\u011f\u0131t\u0131labilir. Belirli bir kullan\u0131c\u0131 grubuna (%10) yeni s\u00fcr\u00fcm\u00fc y\u00f6nlendirerek A\/B testi yap\u0131l\u0131r. Performans metrikleri (yan\u0131t s\u00fcresi, \u00e7\u00f6z\u00fcm oran\u0131, canl\u0131 destek y\u00f6nlendirme say\u0131s\u0131) kar\u015f\u0131la\u015ft\u0131r\u0131larak, yeni s\u00fcr\u00fcm\u00fcn daha iyi sonu\u00e7lar verip vermedi\u011fi belirlenir. Daha sonra, daha iyi olan s\u00fcr\u00fcm t\u00fcm kullan\u0131c\u0131lara yayg\u0131nla\u015ft\u0131r\u0131l\u0131r.<\/li>\n
- \u00d6l\u00e7eklenebilirlik:<\/strong> Black Friday gibi yo\u011fun al\u0131\u015fveri\u015f d\u00f6nemlerinde m\u00fc\u015fteri destek talepleri tavan yapabilir. OLS, Docker ve Kubernetes entegrasyonu sayesinde bu ani y\u00fck art\u0131\u015flar\u0131n\u0131 otomatik olarak y\u00f6netebilir, gerekti\u011finde chatbot instancelar\u0131n\u0131 otomatik olarak art\u0131rarak hizmet kesintisi ya\u015fanmas\u0131n\u0131 engeller.<\/li>\n
- Performans \u0130zleme:<\/strong> OLS'nin yerle\u015fik izleme yetenekleri sayesinde, her bir chatbot oturumunun ne kadar s\u00fcrd\u00fc\u011f\u00fc, hangi ara\u00e7lar\u0131n ka\u00e7 kez \u00e7a\u011fr\u0131ld\u0131\u011f\u0131, LLM yan\u0131t s\u00fcreleri ve olas\u0131 hatalar ger\u00e7ek zamanl\u0131 olarak takip edilir. Anomaliler tespit edildi\u011finde geli\u015ftirici ekibine uyar\u0131lar g\u00f6nderilir, b\u00f6ylece sorunlar proaktif olarak \u00e7\u00f6z\u00fcl\u00fcr.<\/li>\n
- G\u00fcvenlik:<\/strong> M\u00fc\u015fteri verileri ve sipari\u015f bilgileri gibi hassas verilerin korunmas\u0131 esast\u0131r. OLS, API anahtarlar\u0131 ve yetkilendirme mekanizmalar\u0131 ile chatbot API'sine sadece yetkili uygulamalar\u0131n eri\u015fmesini sa\u011flar.<\/li>\n<\/ul>\n
Bu senaryo, LangGraph.js'in dinamik i\u015f ak\u0131\u015flar\u0131 olu\u015fturma g\u00fcc\u00fcn\u00fc ve Open LangGraph Server'\u0131n bu i\u015f ak\u0131\u015flar\u0131n\u0131 g\u00fcvenilir, \u00f6l\u00e7eklenebilir ve y\u00f6netilebilir bir \u015fekilde \u00fcretim ortam\u0131na ta\u015f\u0131ma yetene\u011fini a\u00e7\u0131k\u00e7a g\u00f6stermektedir. \u015eirket, bu sayede hem m\u00fc\u015fteri deneyimini iyile\u015ftirir hem de operasyonel verimlili\u011fini art\u0131r\u0131r, insan kaynaklar\u0131n\u0131 daha karma\u015f\u0131k problemlere y\u00f6nlendirebilir.<\/p>\n
\n Uzman \u0130pucu: M\u00fc\u015fteri destek chatbotunuz i\u00e7in LangGraph.js \u00fczerinde 'hata i\u015fleme' d\u00fc\u011f\u00fcmleri ekleyerek, beklenmedik durumlarda (API hatas\u0131, LLM yan\u0131t\u0131 hatas\u0131) graceful degradation (zarif d\u00fc\u015f\u00fc\u015f) sa\u011flayabilir ve kullan\u0131c\u0131ya uygun bir mesaj sunabilirsiniz. Bu, \u00fcretimde kullan\u0131c\u0131 deneyimini \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131r\u0131r.\n <\/div>\n\u00dcretim Ortam\u0131nda Optimizasyon ve Geli\u015fmi\u015f \u00d6zellikler Nelerdir?<\/h3>\n
LangGraph.js i\u015f ak\u0131\u015flar\u0131n\u0131z\u0131 Open LangGraph Server ile \u00fcretime ald\u0131ktan sonra bile, uygulaman\u0131z\u0131n performans\u0131n\u0131 ve kararl\u0131l\u0131\u011f\u0131n\u0131 s\u00fcrekli olarak optimize etmek ve geli\u015fmi\u015f \u00f6zelliklerden faydalanmak \u00f6nemlidir. \u00dcretim ortam\u0131nda kar\u015f\u0131la\u015faca\u011f\u0131n\u0131z zorluklar ve bunlara y\u00f6nelik \u00e7\u00f6z\u00fcmler, AI tabanl\u0131 uygulaman\u0131z\u0131n uzun \u00f6m\u00fcrl\u00fc ve ba\u015far\u0131l\u0131 olmas\u0131n\u0131 sa\u011flar.<\/p>\n
1. Performans Optimizasyonu<\/h4>\n\n- \u00d6nbellekleme (Caching):<\/strong> S\u0131k\u00e7a sorgulanan veya pahal\u0131 LLM \u00e7a\u011fr\u0131lar\u0131 i\u00e7eren d\u00fc\u011f\u00fcmlerin sonu\u00e7lar\u0131n\u0131 \u00f6nbelle\u011fe al\u0131n. Redis gibi bir \u00f6nbellekleme sistemi kullanarak, ayn\u0131 girdiye sahip tekrarlayan \u00e7a\u011fr\u0131larda LLM'yi tekrar \u00e7al\u0131\u015ft\u0131rmaktan ka\u00e7\u0131nabilirsiniz. Bu, hem maliyetleri d\u00fc\u015f\u00fcr\u00fcr hem de yan\u0131t s\u00fcrelerini iyile\u015ftirir. \u00d6rne\u011fin, bir bilgi taban\u0131 aramas\u0131 belirli bir anahtar kelime i\u00e7in her zaman ayn\u0131 sonucu d\u00f6nd\u00fcrecekse, bu sonucu \u00f6nbelle\u011fe almak mant\u0131kl\u0131d\u0131r.<\/li>\n
- Toplu \u0130\u015fleme (Batching):<\/strong> M\u00fcmk\u00fcn oldu\u011funda, birden fazla iste\u011fi tek bir LLM \u00e7a\u011fr\u0131s\u0131nda birle\u015ftirmeye \u00e7al\u0131\u015f\u0131n. Baz\u0131 LLM sa\u011flay\u0131c\u0131lar\u0131 toplu i\u015fleme API'leri sunar ve bu, gecikmeyi azaltarak verimlili\u011fi art\u0131rabilir. Ancak, LangGraph'\u0131n durum bilgisi yap\u0131s\u0131 nedeniyle bu her zaman m\u00fcmk\u00fcn olmayabilir.<\/li>\n
- Asenkron \u0130\u015flem:<\/strong> LangGraph.js, asenkron i\u015flemleri do\u011fal olarak destekler. Uzun s\u00fcreli g\u00f6revleri (\u00f6rne\u011fin b\u00fcy\u00fck veritaban\u0131 sorgular\u0131 veya karma\u015f\u0131k API \u00e7a\u011fr\u0131lar\u0131) bloke etmeyecek \u015fekilde tasarlay\u0131n. Node.js'in olay d\u00f6ng\u00fcs\u00fcn\u00fc etkin bir \u015fekilde kullanarak uygulaman\u0131z\u0131n duyarl\u0131 kalmas\u0131n\u0131 sa\u011flay\u0131n.<\/li>\n
- Minimum Ba\u011f\u0131ml\u0131l\u0131k:<\/strong> \u0130\u015f ak\u0131\u015f\u0131n\u0131zda sadece ger\u00e7ekten gerekli olan ba\u011f\u0131ml\u0131l\u0131klar\u0131 kullan\u0131n. Her ek ba\u011f\u0131ml\u0131l\u0131k, paket boyutunu ve ba\u015flang\u0131\u00e7 s\u00fcresini art\u0131r\u0131r.<\/li>\n<\/ul>\n
2. G\u00f6zlemlenebilirlik (Observability)<\/h4>\n
\u00dcretimdeki bir uygulaman\u0131n davran\u0131\u015f\u0131n\u0131 anlamak i\u00e7in kapsaml\u0131 g\u00f6zlemlenebilirlik ara\u00e7lar\u0131 \u015fartt\u0131r:<\/p>\n
\n- G\u00fcnl\u00fckleme (Logging):<\/strong> Her d\u00fc\u011f\u00fcm ge\u00e7i\u015fini, LLM \u00e7a\u011fr\u0131s\u0131n\u0131, ara\u00e7 kullan\u0131m\u0131n\u0131 ve \u00f6zellikle hata durumlar\u0131n\u0131 detayl\u0131 bir \u015fekilde g\u00fcnl\u00fc\u011fe kaydedin. Yap\u0131land\u0131r\u0131lm\u0131\u015f g\u00fcnl\u00fckler (JSON format\u0131nda) kullanmak, loglar\u0131n\u0131z\u0131 Elasticsearch, Splunk veya Datadog gibi merkezi g\u00fcnl\u00fck y\u00f6netim sistemlerinde daha kolay analiz etmenizi sa\u011flar.<\/li>\n
- \u0130zleme (Tracing):<\/strong> LangChain\/LangGraph, OpenTelemetry gibi standartlara uygun izleme yetenekleri sunar. Her bir iste\u011fin t\u00fcm ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc (hangi d\u00fc\u011f\u00fcmlerden ge\u00e7ti, LLM \u00e7a\u011fr\u0131lar\u0131 ne kadar s\u00fcrd\u00fc vb.) u\u00e7tan uca izlemek, performans darbo\u011fazlar\u0131n\u0131 veya hatalar\u0131n kayna\u011f\u0131n\u0131 tespit etmek i\u00e7in kritik \u00f6neme sahiptir. LangSmith gibi ara\u00e7lar bu konuda b\u00fcy\u00fck kolayl\u0131k sa\u011flar.<\/li>\n
- Metrikler (Metrics):<\/strong> \u0130\u015f ak\u0131\u015f\u0131 ba\u015flatma say\u0131s\u0131, hata oran\u0131, ortalama yan\u0131t s\u00fcresi, belirli d\u00fc\u011f\u00fcmlerin \u00e7al\u0131\u015fma s\u00fcresi gibi metrikleri toplay\u0131n. Prometheus ve Grafana gibi ara\u00e7lar kullanarak bu metrikleri g\u00f6rselle\u015ftirin ve uyar\u0131lar ayarlayarak anormallikler hakk\u0131nda bilgilendirilmenizi sa\u011flay\u0131n.<\/li>\n<\/ul>\n
3. CI\/CD Entegrasyonu<\/h4>\n
Open LangGraph Server'\u0131n CLI ve API'si, i\u015f ak\u0131\u015flar\u0131n\u0131z\u0131n da\u011f\u0131t\u0131m\u0131n\u0131 ve y\u00f6netimini CI\/CD (S\u00fcrekli Entegrasyon\/S\u00fcrekli Da\u011f\u0131t\u0131m) boru hatlar\u0131n\u0131za entegre etmenizi sa\u011flar. Her kod de\u011fi\u015fikli\u011finde otomatik testleri \u00e7al\u0131\u015ft\u0131r\u0131n, i\u015f ak\u0131\u015f\u0131n\u0131z\u0131 derleyin ve OLS'ye yeni bir s\u00fcr\u00fcm olarak da\u011f\u0131t\u0131n. Bu, manuel hatalar\u0131 azalt\u0131r ve yeni \u00f6zelliklerin daha h\u0131zl\u0131 ve g\u00fcvenilir bir \u015fekilde \u00fcretime al\u0131nmas\u0131n\u0131 sa\u011flar.<\/p>\n
4. G\u00fcvenlik En \u0130yi Uygulamalar\u0131<\/h4>\n\n- S\u0131r Y\u00f6netimi:<\/strong> API anahtarlar\u0131, veritaban\u0131 kimlik bilgileri ve di\u011fer hassas bilgileri \u00e7evre de\u011fi\u015fkenleri (environment variables), Kubernetes s\u0131rlar\u0131 (secrets) veya AWS Secrets Manager gibi g\u00fcvenli s\u0131r y\u00f6netim sistemleri arac\u0131l\u0131\u011f\u0131yla y\u00f6netin. Asla koduza sabit kodlamay\u0131n.<\/li>\n
- Eri\u015fim Kontrol\u00fc:<\/strong> OLS'nin sundu\u011fu API anahtarlar\u0131 veya JWT (JSON Web Token) tabanl\u0131 kimlik do\u011frulama mekanizmalar\u0131n\u0131 kullanarak API endpoint'lerinize eri\u015fimi k\u0131s\u0131tlay\u0131n. Minimum yetki ilkesini uygulay\u0131n.<\/li>\n
- Girdi Do\u011frulama:<\/strong> M\u00fc\u015fteriden gelen t\u00fcm girdileri (LLM'e g\u00f6ndermeden \u00f6nce) temizleyin ve do\u011frulay\u0131n. Prompt enjeksiyonu gibi g\u00fcvenlik a\u00e7\u0131klar\u0131n\u0131 \u00f6nlemek i\u00e7in dikkatli olun.<\/li>\n<\/ul>\n
5. \u00c7oklu Ortam (Multi-Tenant) Deste\u011fi<\/h4>\n
E\u011fer uygulaman\u0131z farkl\u0131 m\u00fc\u015fterilere veya kullan\u0131c\u0131lara hizmet veriyorsa, her bir kullan\u0131c\u0131n\u0131n veya kirac\u0131n\u0131n (tenant) kendi \u00f6zel i\u015f ak\u0131\u015f\u0131 durumuna sahip oldu\u011fundan emin olun. OLS'nin thread_id<\/code> veya benzeri yap\u0131land\u0131r\u0131labilir parametreleri, farkl\u0131 kullan\u0131c\u0131lar aras\u0131nda durum ayr\u0131m\u0131n\u0131 kolayla\u015ft\u0131r\u0131r. Veri izolasyonu ve g\u00fcvenlik politikalar\u0131na \u00f6zellikle dikkat edin.<\/p>\nBu geli\u015fmi\u015f stratejileri uygulayarak, LangGraph.js ve Open LangGraph Server ile olu\u015fturdu\u011funuz LLM uygulamalar\u0131n\u0131n sadece i\u015flevsel de\u011fil, ayn\u0131 zamanda \u00fcretimde g\u00fc\u00e7l\u00fc, g\u00fcvenli ve s\u00fcrd\u00fcr\u00fclebilir olmas\u0131n\u0131 sa\u011flayabilirsiniz.<\/p>\n
Sonu\u00e7: Gelece\u011fin \u00dcretim Ortamlar\u0131 \u0130\u00e7in G\u00fc\u00e7l\u00fc Bir \u0130kili<\/h2>\n
B\u00fcy\u00fck dil modellerinin (LLM) y\u00fckseli\u015fiyle birlikte, yapay zeka destekli uygulamalar\u0131n geli\u015ftirilmesi ve \u00fcretim ortam\u0131na da\u011f\u0131t\u0131lmas\u0131 hi\u00e7 olmad\u0131\u011f\u0131 kadar karma\u015f\u0131k hale gelmi\u015ftir. LangGraph.js, bu karma\u015f\u0131kl\u0131\u011f\u0131 soyutlayarak geli\u015ftiricilerin sezgisel ve g\u00fc\u00e7l\u00fc i\u015f ak\u0131\u015flar\u0131 olu\u015fturmas\u0131na olanak tan\u0131rken, Open LangGraph Server (OLS) ise bu i\u015f ak\u0131\u015flar\u0131n\u0131 \u00fcretimde g\u00fcvenilir, \u00f6l\u00e7eklenebilir ve y\u00f6netilebilir bir \u015fekilde sunman\u0131n anahtar\u0131n\u0131 elinde tutar. Bu iki teknolojinin birle\u015fimi, modern AI uygulamalar\u0131n\u0131n ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc basitle\u015ftiren ve h\u0131zland\u0131ran g\u00fc\u00e7l\u00fc bir sinerji yarat\u0131r.<\/p>\n
Makale boyunca ele ald\u0131\u011f\u0131m\u0131z gibi, LangGraph.js ile durum bilgisi olan, dinamik ve reaktif LLM i\u015f ak\u0131\u015flar\u0131 olu\u015fturmak olduk\u00e7a kolayd\u0131r. D\u00fc\u011f\u00fcmler ve kenarlar arac\u0131l\u0131\u011f\u0131yla karar verme s\u00fcre\u00e7leri, ara\u00e7 kullan\u0131m\u0131 ve \u00e7ok a\u015famal\u0131 etkile\u015fimler \u015feffaf bir \u015fekilde modellenebilir. Bu, geleneksel programlama yakla\u015f\u0131mlar\u0131yla y\u00f6netilmesi zor olan karma\u015f\u0131k ajan tabanl\u0131 sistemlerin geli\u015ftirilmesini demokratikle\u015ftirir. \u00d6rnek m\u00fc\u015fteri destek chatbotu senaryosu, bu esnekli\u011fin ger\u00e7ek d\u00fcnyada nas\u0131l de\u011fer yaratt\u0131\u011f\u0131n\u0131 somut bir \u015fekilde g\u00f6stermi\u015ftir.<\/p>\n
\u00d6te yandan, Open LangGraph Server, geli\u015ftirilen bu i\u015f ak\u0131\u015flar\u0131n\u0131 sadece prototip a\u015famas\u0131nda b\u0131rakmakla kalmay\u0131p, onlar\u0131 ger\u00e7ek kullan\u0131c\u0131lar\u0131n ihtiya\u00e7lar\u0131n\u0131 kar\u015f\u0131layacak \u015fekilde \u00fcretime ta\u015f\u0131ma yetene\u011fi sunar. S\u00fcr\u00fcmleme, A\/B testi, otomatik \u00f6l\u00e7eklendirme, kapsaml\u0131 izleme ve g\u00fc\u00e7l\u00fc g\u00fcvenlik \u00f6zellikleri, OLS'yi LLM uygulamalar\u0131 i\u00e7in vazge\u00e7ilmez bir platform haline getirir. Geli\u015ftiriciler, altyap\u0131sal endi\u015felerden ziyade i\u015f mant\u0131\u011f\u0131na odaklanabilir, bu da inovasyonu h\u0131zland\u0131r\u0131r ve pazar s\u00fcresini k\u0131salt\u0131r.<\/p>\n
Gelecekte, AI destekli uygulamalar\u0131n daha da yayg\u0131nla\u015faca\u011f\u0131 ve karma\u015f\u0131kla\u015faca\u011f\u0131 d\u00fc\u015f\u00fcn\u00fcld\u00fc\u011f\u00fcnde, LangGraph.js ve Open LangGraph Server gibi ara\u00e7lara olan ihtiya\u00e7 artacakt\u0131r. Bu g\u00fc\u00e7l\u00fc ikili, geli\u015ftiricilere, yenilik\u00e7i AI \u00e7\u00f6z\u00fcmlerini g\u00fcvenle tasarlama, da\u011f\u0131tma ve y\u00f6netme yetene\u011fi vererek, yapay zekan\u0131n tam potansiyelini ortaya \u00e7\u0131karmalar\u0131na yard\u0131mc\u0131 olacakt\u0131r. Bu teknolojileri benimseyerek, sadece bug\u00fcn\u00fcn zorluklar\u0131n\u0131 a\u015fmakla kalmaz, ayn\u0131 zamanda yar\u0131n\u0131n AI inovasyonlar\u0131 i\u00e7in sa\u011flam bir temel olu\u015fturursunuz.<\/p>\n
S\u0131k\u00e7a Sorulan Sorular<\/h3>\n\n- \n LangGraph.js ve LangChain aras\u0131ndaki fark nedir?<\/strong><\/p>\n
LangChain, b\u00fcy\u00fck dil modelleri (LLM'ler) ile uygulamalar olu\u015fturmak i\u00e7in genel bir \u00e7er\u00e7evedir. Ara\u00e7lar, modeller, prompt \u015fablonlar\u0131 gibi bir\u00e7ok bile\u015feni i\u00e7erir. LangGraph.js ise LangChain ekosisteminin bir uzant\u0131s\u0131d\u0131r ve \u00f6zellikle durum bilgisi olan (stateful) \u00e7ok a\u015famal\u0131 LLM uygulamalar\u0131 (ajanlar ve \u00e7ok akt\u00f6rl\u00fc sistemler gibi) olu\u015fturmak i\u00e7in graf teorisi tabanl\u0131 bir yakla\u015f\u0131m sunar. Yani, LangGraph.js daha \u00e7ok karma\u015f\u0131k ve d\u00f6ng\u00fcsel i\u015f ak\u0131\u015flar\u0131n\u0131n y\u00f6netimi konusunda uzmanla\u015fm\u0131\u015ft\u0131r, LangChain ise daha geni\u015f bir ara\u00e7 setidir.<\/p>\n<\/li>\n
- \n Open LangGraph Server'\u0131 kullanmak ne kadar maliyetli?<\/strong><\/p>\n
Open LangGraph Server, a\u00e7\u0131k kaynakl\u0131 bir projedir, yani yaz\u0131l\u0131m\u0131n kendisi \u00fccretsizdir. Ancak, onu \u00fcretim ortam\u0131nda \u00e7al\u0131\u015ft\u0131rmak i\u00e7in bir bulut sa\u011flay\u0131c\u0131s\u0131nda (AWS, Azure, GCP vb.) sunucu, veritaban\u0131 ve di\u011fer altyap\u0131 hizmetleri i\u00e7in maliyetleriniz olacakt\u0131r. Bu maliyetler, uygulaman\u0131z\u0131n trafi\u011fine, \u00f6l\u00e7e\u011fine ve kulland\u0131\u011f\u0131n\u0131z bulut kaynaklar\u0131na g\u00f6re de\u011fi\u015fiklik g\u00f6sterecektir.<\/p>\n<\/li>\n
- \n LangGraph.js i\u015f ak\u0131\u015flar\u0131m\u0131 Python ile de yazabilir miyim?<\/strong><\/p>\n
Evet, LangGraph'\u0131n hem Python hem de JavaScript\/TypeScript i\u00e7in uygulamalar\u0131 mevcuttur. Bu makale LangGraph.js (JavaScript versiyonu) \u00fczerine odaklanm\u0131\u015ft\u0131r, ancak temeldeki kavramlar ve mimari her iki dilde de benzerdir. Hangi versiyonu kullanaca\u011f\u0131n\u0131z, projenizin mevcut teknoloji y\u0131\u011f\u0131n\u0131na ve geli\u015ftirici ekibinizin tercihine ba\u011fl\u0131d\u0131r.<\/p>\n<\/li>\n
- \n Open LangGraph Server g\u00fcvenlik a\u00e7\u0131s\u0131ndan ne gibi \u00f6zellikler sunuyor?<\/strong><\/p>\n
Open LangGraph Server, \u00fcretim ortam\u0131nda g\u00fcvenlik i\u00e7in \u00e7e\u015fitli \u00f6zellikler sunar. Bunlar aras\u0131nda API anahtar\u0131 veya JWT tabanl\u0131 kimlik do\u011frulama ve yetkilendirme mekanizmalar\u0131, hassas bilgileri (API anahtarlar\u0131 gibi) \u00e7evre de\u011fi\u015fkenleri veya s\u0131r y\u00f6netim sistemleri arac\u0131l\u0131\u011f\u0131yla y\u00f6netme yetene\u011fi ve potansiyel g\u00fcvenlik a\u00e7\u0131klar\u0131n\u0131 azaltmak i\u00e7in d\u00fczenli g\u00fcncellemeler ve topluluk deste\u011fi bulunmaktad\u0131r. Ayr\u0131ca, OLS'yi konteynerize ederek (Docker) ve g\u00fcvenli a\u011f konfig\u00fcrasyonlar\u0131 uygulayarak ek g\u00fcvenlik katmanlar\u0131 ekleyebilirsiniz.<\/p>\n<\/li>\n
- \n Open LangGraph Server'\u0131 kendi mevcut altyap\u0131ma entegre edebilir miyim?<\/strong><\/p>\n
Kesinlikle! Open LangGraph Server, standart bir HTTP API'si olarak \u00e7al\u0131\u015ft\u0131\u011f\u0131 i\u00e7in, mevcut mikroservis mimarilerinize, web uygulamalar\u0131n\u0131za veya herhangi bir backend sisteminize kolayca entegre edilebilir. Docker konteynerleri arac\u0131l\u0131\u011f\u0131yla da\u011f\u0131t\u0131lmas\u0131, Kubernetes gibi konteyner orkestrasyon ara\u00e7lar\u0131yla uyumlu olmas\u0131n\u0131 sa\u011flar ve bu da mevcut CI\/CD boru hatlar\u0131n\u0131za sorunsuz bir \u015fekilde ba\u011flanabilece\u011fi anlam\u0131na gelir. Bu esneklik, OLS'yi bir\u00e7ok farkl\u0131 altyap\u0131 ortam\u0131nda de\u011ferli bir \u00e7\u00f6z\u00fcm haline getirir.<\/p>\n<\/li>\n<\/ul>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"B\u00fcy\u00fck dil modelleri (LLM’ler) tabanl\u0131 uygulamalar\u0131 geli\u015ftirirken kar\u015f\u0131la\u015f\u0131lan en b\u00fcy\u00fck zorluklardan biri, karma\u015f\u0131k i\u015f ak\u0131\u015flar\u0131n\u0131 \u00fcretim ortam\u0131na g\u00fcvenilir…","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":[874],"tags":[],"class_list":{"0":"post-34434","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-server","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n
LangGraph.js \u0130\u015f Ak\u0131\u015flar\u0131n\u0131 Open LangGraph Server ile \u00dcretimde Devreye Alma Rehberi<\/title>\n<meta name=\"description\" content=\"B\u00fcy\u00fck dil modelleri (LLM'ler) tabanl\u0131 uygulamalar\u0131 geli\u015ftirirken kar\u015f\u0131la\u015f\u0131lan en b\u00fcy\u00fck zorluklardan biri, karma\u015f\u0131k i\u015f ak\u0131\u015flar\u0131n\u0131 \u00fcretim ortam\u0131na g\u00fcvenilir ve \u00f6l\u00e7eklenebilir bir \u015fekilde da\u011f\u0131tmakt\u0131r. 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