Langchain ile Ba\u011flam\u0131 Koruyan Sohbet Botu Geli\u015ftirme<\/p>\n
G\u00fcn\u00fcm\u00fczde artan veri hacmi ve karma\u015f\u0131k kullan\u0131c\u0131 etkile\u015fimleri, basit sohbet botlar\u0131ndan \u00e7ok daha fazlas\u0131n\u0131 gerektirir. Kullan\u0131c\u0131yla olan etkile\u015fim boyunca ba\u011flam\u0131 hat\u0131rlayan, \u00f6nceki konu\u015fmalar\u0131 anlayan ve buna g\u00f6re yan\u0131t veren bir sohbet botu geli\u015ftirmek, hem kullan\u0131c\u0131 deneyimini iyile\u015ftirir hem de daha ak\u0131ll\u0131 uygulamalar olu\u015fturmaya olanak tan\u0131r. Bu makalede, Python tabanl\u0131 g\u00fc\u00e7l\u00fc bir k\u00fct\u00fcphane olan Langchain kullanarak ba\u011flam\u0131 koruyan bir sohbet botu nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 ad\u0131m ad\u0131m ele alaca\u011f\u0131z. Langchain’in sundu\u011fu ara\u00e7lar ve teknikler sayesinde, karma\u015f\u0131k sohbet senaryolar\u0131n\u0131 bile kolayca y\u00f6netebileceksiniz.<\/p>\n
Langchain’i kullanarak ba\u011flam\u0131 koruyan bir sohbet botu geli\u015ftirmek i\u00e7in \u00f6ncelikle birka\u00e7 temel kavram\u0131 anlamam\u0131z gerekir. Bunlar aras\u0131nda b\u00fcy\u00fck dil modelleri (LLM’ler), vekt\u00f6r veritabanlar\u0131 ve haf\u0131za y\u00f6netimi yer al\u0131r. LLM’ler, metin \u00fcretme, sorular\u0131 cevaplama ve \u00e7eviri gibi g\u00f6revleri yerine getiren g\u00fc\u00e7l\u00fc yapay zeka modelleridir. OpenAI’nin GPT modelleri, Google’\u0131n PaLM 2’si veya benzerleri bu kategoriye girer. Vekt\u00f6r veritabanlar\u0131 ise, metin par\u00e7alar\u0131n\u0131 matematiksel vekt\u00f6rlere d\u00f6n\u00fc\u015ft\u00fcrerek benzerlik kar\u015f\u0131la\u015ft\u0131rmalar\u0131n\u0131 h\u0131zland\u0131r\u0131r. Bu sayede, bot daha \u00f6nceki konu\u015fmalardan ilgili bilgileri daha h\u0131zl\u0131 bir \u015fekilde bulabilir. Son olarak, haf\u0131za y\u00f6netimi, botun konu\u015fma ba\u011flam\u0131n\u0131 etkili bir \u015fekilde nas\u0131l izleyece\u011fini ve koruyaca\u011f\u0131n\u0131 belirler.<\/p>\n
Langchain, bu bile\u015fenleri bir araya getirerek karma\u015f\u0131k sohbet botlar\u0131n\u0131 geli\u015ftirmeyi kolayla\u015ft\u0131r\u0131r. K\u00fct\u00fcphanenin sundu\u011fu farkl\u0131 haf\u0131za mekanizmalar\u0131 sayesinde, k\u0131sa s\u00fcreli veya uzun s\u00fcreli ba\u011flam y\u00f6netimi yap\u0131labilir. \u00d6rne\u011fin, k\u0131sa s\u00fcreli haf\u0131za, son birka\u00e7 konu\u015fmay\u0131 hat\u0131rlamak i\u00e7in kullan\u0131l\u0131rken, uzun s\u00fcreli haf\u0131za, daha eski konu\u015fmalar\u0131n kay\u0131tlar\u0131n\u0131 tutar ve gerekti\u011finde bunlara eri\u015fim sa\u011flar. Langchain’in esnek yap\u0131s\u0131, farkl\u0131 LLM’ler ve vekt\u00f6r veritabanlar\u0131yla kolayca entegre olman\u0131za olanak tan\u0131r, b\u00f6ylece ihtiya\u00e7lar\u0131n\u0131za en uygun sistemi kurabilirsiniz.<\/p>\n
\u015eimdi, Langchain kullanarak basit bir ba\u011flam\u0131 koruyan sohbet botu olu\u015ftural\u0131m. Bu \u00f6rnekte, OpenAI’nin GPT-3.5-turbo modelini ve bir haf\u0131za mekanizmas\u0131n\u0131 kullanaca\u011f\u0131z. A\u015fa\u011f\u0131daki kod blo\u011fu, bu i\u015flemi ad\u0131m ad\u0131m g\u00f6stermektedir:<\/p>\n
from langchain.chat_models import ChatOpenAI\nfrom langchain.memory import ConversationBufferMemory\nfrom langchain.chains import ConversationChain\n\n# OpenAI API anahtar\u0131n\u0131z\u0131 buraya ekleyin\nopenai_api_key = \"YOUR_OPENAI_API_KEY\"\n\n# ChatOpenAI modelini ba\u015flat\u0131n\nllm = ChatOpenAI(temperature=0, model_name=\"gpt-3.5-turbo\", openai_api_key=openai_api_key)\n\n# Konu\u015fma haf\u0131zas\u0131n\u0131 olu\u015fturun\nmemory = ConversationBufferMemory()\n\n# Konu\u015fma zincirini olu\u015fturun\nconversation = ConversationChain(llm=llm, memory=memory)\n\n# Kullan\u0131c\u0131yla etkile\u015fim\nwhile True:\n user_input = input(\"Kullan\u0131c\u0131: \")\n if user_input.lower() == \"\u00e7\u0131k\u0131\u015f\":\n break\n response = conversation.predict(input=user_input)\n print(\"Bot:\", response)<\/pre>\n<\/code><\/p>\n
Bu kodda, \u00f6ncelikle gerekli k\u00fct\u00fcphaneleri i\u00e7e aktar\u0131yoruz. Daha sonra, OpenAI API anahtar\u0131n\u0131z\u0131 ve kullan\u0131lacak modeli belirliyoruz. ConversationBufferMemory<\/code>, konu\u015fman\u0131n ge\u00e7mi\u015fini saklamak i\u00e7in kullan\u0131l\u0131r. ConversationChain<\/code>, LLM ve haf\u0131za mekanizmas\u0131n\u0131 birle\u015ftirerek sohbet ak\u0131\u015f\u0131n\u0131 y\u00f6netir. Son olarak, kullan\u0131c\u0131 girdilerini al\u0131p, bot yan\u0131tlar\u0131n\u0131 yazd\u0131ran bir d\u00f6ng\u00fc olu\u015fturulur. \"\u00e7\u0131k\u0131\u015f\" yazana kadar bot \u00e7al\u0131\u015fmaya devam eder.<\/p>\nDaha Geli\u015fmi\u015f Haf\u0131za Y\u00f6netimi Teknikleri<\/h2>\n
Basit ConversationBufferMemory<\/code> k\u0131sa s\u00fcreli haf\u0131za i\u00e7in yeterli olabilir, ancak daha uzun ve karma\u015f\u0131k konu\u015fmalar i\u00e7in daha geli\u015fmi\u015f tekniklere ihtiya\u00e7 duyabilirsiniz. Langchain, ConversationSummaryMemory<\/code> gibi farkl\u0131 haf\u0131za mekanizmalar\u0131 sunar. Bu mekanizma, konu\u015fman\u0131n \u00f6zetini tutarak uzun s\u00fcreli ba\u011flam\u0131 korur. Ayr\u0131ca, VectorStoreMemory<\/code> gibi vekt\u00f6r veritabanlar\u0131n\u0131 kullanarak \u00e7ok daha b\u00fcy\u00fck bir bilgi taban\u0131yla \u00e7al\u0131\u015fabilirsiniz. Bu sayede, bot ge\u00e7mi\u015f konu\u015fmalardan ilgili bilgileri arayabilir ve daha ak\u0131ll\u0131 yan\u0131tlar \u00fcretebilir.<\/p>\nVekt\u00f6r Veritabanlar\u0131yla Ba\u011flam Geni\u015fletme<\/h2>\n
Vekt\u00f6r veritabanlar\u0131, metin benzerli\u011fi arama i\u015flemlerini h\u0131zland\u0131rarak daha geli\u015fmi\u015f bir ba\u011flam y\u00f6netimi sa\u011flar. Langchain, Chroma, FAISS veya Pinecone gibi farkl\u0131 vekt\u00f6r veritabanlar\u0131yla entegrasyon imkan\u0131 sunar. Bu veritabanlar\u0131na konu\u015fma ge\u00e7mi\u015fini kaydederek, bot daha \u00f6nceki konu\u015fmalarla ilgili bilgileri daha h\u0131zl\u0131 ve etkili bir \u015fekilde arayabilir. \u00d6rne\u011fin, bir m\u00fc\u015fteri destek botu ge\u00e7mi\u015fteki ileti\u015fimleri inceleyerek m\u00fc\u015fteri profilini \u00e7\u0131karabilir ve daha ki\u015fiselle\u015ftirilmi\u015f bir hizmet sunabilir.<\/p>\n
from langchain.vectorstores import Chroma\nfrom langchain.embeddings import OpenAIEmbeddings\n# ... (\u00f6nceki kodlar) ...\n\n# Vekt\u00f6r veritaban\u0131 olu\u015fturma\nembeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)\ndb = Chroma.from_texts([\"Bu bir test metnidir.\"], embeddings)\n\n# ... (kullan\u0131lacak memory mekanizmas\u0131n\u0131 de\u011fi\u015ftirin) ...<\/pre>\n<\/code><\/p>\n
Bu \u00f6rnekte, Chroma veritaban\u0131 ve OpenAI'nin g\u00f6m\u00fcl\u00fc temsillerini kullanarak bir vekt\u00f6r veritaban\u0131 olu\u015fturduk. Daha sonra, ConversationChain<\/code> i\u00e7inde VectorStoreMemory<\/code> ile entegre ederek ge\u00e7mi\u015f konu\u015fmalar\u0131 bu veritaban\u0131nda saklayabilirsiniz. Bu \u015fekilde, daha uzun ve karma\u015f\u0131k konu\u015fmalarda bile ilgili bilgileri kolayca bulabilirsiniz.<\/p>\nGer\u00e7ek D\u00fcnya Senaryolar\u0131: Vaka Analizi<\/h2>\n
Bir e-ticaret sitesi i\u00e7in bir m\u00fc\u015fteri destek botu d\u00fc\u015f\u00fcnelim. Kullan\u0131c\u0131 bir \u00fcr\u00fcn hakk\u0131nda bilgi istedi\u011finde, bot sadece \u00fcr\u00fcn \u00f6zelliklerini de\u011fil, ayn\u0131 zamanda \u00f6nceki konu\u015fmalardan elde etti\u011fi kullan\u0131c\u0131n\u0131n tercihlerini de g\u00f6z \u00f6n\u00fcnde bulundurmal\u0131d\u0131r. \u00d6rne\u011fin, kullan\u0131c\u0131 daha \u00f6nce k\u0131rm\u0131z\u0131 renk \u00fcr\u00fcnlere ilgi g\u00f6sterdiyse, bot k\u0131rm\u0131z\u0131 renkteki benzer \u00fcr\u00fcnleri \u00f6nerebilir. Bu, VectorStoreMemory<\/code> kullanarak m\u00fcmk\u00fcn olabilir. Vekt\u00f6r veritaban\u0131na \u00fcr\u00fcn \u00f6zellikleri, m\u00fc\u015fteri tercihleri ve \u00f6nceki konu\u015fmalar kaydedilir. Bot, gelen soruyu i\u015flerken, veritaban\u0131nda ilgili bilgileri arar ve daha ki\u015fiselle\u015ftirilmi\u015f bir yan\u0131t \u00fcretir.<\/p>\n\u00d6\u011frenme Yol Haritas\u0131<\/h2>\n
Yeni Ba\u015flayan:<\/strong> Bu makaledeki basit \u00f6rneklerle ba\u015flayarak Langchain'in temel fonksiyonlar\u0131n\u0131 ve haf\u0131za mekanizmalar\u0131n\u0131 \u00f6\u011frenin. Farkl\u0131 LLM'leri deneyerek performanslar\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131r\u0131n.<\/p>\nOrta Seviye:<\/strong> ConversationSummaryMemory<\/code> ve VectorStoreMemory<\/code> gibi geli\u015fmi\u015f haf\u0131za mekanizmalar\u0131n\u0131 kullanarak daha karma\u015f\u0131k sohbet botlar\u0131 olu\u015fturun. Farkl\u0131 vekt\u00f6r veritabanlar\u0131n\u0131 deneyerek performanslar\u0131n\u0131 analiz edin. Daha fazla \u00f6zelle\u015ftirme i\u00e7in Langchain'in parametrelerini inceleyin.<\/p>\n\u0130leri D\u00fczey:<\/strong> Kendi \u00f6zel haf\u0131za mekanizmalar\u0131n\u0131z\u0131 olu\u015fturun. LLM'ler ile vekt\u00f6r veritabanlar\u0131n\u0131 daha etkili bir \u015fekilde entegre edin. Karma\u015f\u0131k ba\u011flam y\u00f6netimi gerektiren ger\u00e7ek d\u00fcnya senaryolar\u0131 i\u00e7in \u00e7\u00f6z\u00fcmler geli\u015ftirin. \u00d6rne\u011fin, bir chatbot'a uzun metinleri \u00f6zetleme veya birden fazla veri kayna\u011f\u0131ndan bilgi toplayarak sorular\u0131 cevaplama gibi yetenekler kazand\u0131rabilirsiniz. Daha fazla bilgi i\u00e7in [https:\/\/fatihsoysal.com](https:\/\/fatihsoysal.com) inceleyebilirsiniz.<\/p>\nPerformans Kar\u015f\u0131la\u015ft\u0131rmas\u0131<\/h2>\n
| Haf\u0131za Mekanizmas\u0131 | Bellek Kullan\u0131m\u0131 | Yan\u0131t H\u0131z\u0131 | Ba\u011flam Tutma Kalitesi |
\n|---|---|---|---|
\n| ConversationBufferMemory<\/code> | D\u00fc\u015f\u00fck | Y\u00fcksek | D\u00fc\u015f\u00fck |
\n| ConversationSummaryMemory<\/code> | Orta | Orta | Orta |
\n| VectorStoreMemory<\/code> (Chroma) | Y\u00fcksek | D\u00fc\u015f\u00fck | Y\u00fcksek |<\/p>\nDikkat! G\u00fcvenlik Uyar\u0131lar\u0131<\/h2>\n
* OpenAI API anahtar\u0131n\u0131z\u0131 g\u00fcvenli bir \u015fekilde saklay\u0131n. Kodunuzu GitHub gibi platformlarda payla\u015f\u0131rken API anahtar\u0131n\u0131z\u0131 gizleyin.
\n* Kullan\u0131c\u0131 girdilerini do\u011frulay\u0131n ve zararl\u0131 i\u00e7eriklere kar\u015f\u0131 \u00f6nlem al\u0131n. Langchain'in sa\u011flad\u0131\u011f\u0131 g\u00fcvenlik mekanizmalar\u0131n\u0131 kullan\u0131n.
\n* B\u00fcy\u00fck dil modelleri \u00f6nyarg\u0131lar i\u00e7erebilir. \u00dcretti\u011fi yan\u0131tlar\u0131 kontrol edin ve gerekti\u011finde d\u00fczeltmeler yap\u0131n.<\/p>\n
Sonu\u00e7<\/h2>\n
Langchain, ba\u011flam\u0131 koruyan sohbet botlar\u0131 geli\u015ftirmek i\u00e7in g\u00fc\u00e7l\u00fc bir ara\u00e7t\u0131r. Bu makalede ele ald\u0131\u011f\u0131m\u0131z teknikler ve \u00f6rnekler sayesinde, kendi ihtiya\u00e7lar\u0131n\u0131za g\u00f6re \u00f6zelle\u015ftirilebilir ve geli\u015fmi\u015f sohbet botlar\u0131 olu\u015fturabilirsiniz. Unutmay\u0131n ki, ba\u015far\u0131 i\u00e7in farkl\u0131 haf\u0131za mekanizmalar\u0131n\u0131 deneyerek, performanslar\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131rarak ve kullan\u0131c\u0131 deneyimini s\u00fcrekli iyile\u015ftirerek en uygun \u00e7\u00f6z\u00fcm\u00fc bulman\u0131z gerekmektedir.<\/p>\n
S\u0131k\u00e7a Sorulan Sorular<\/h3>\n
* Langchain'i kurmak i\u00e7in hangi ad\u0131mlar\u0131 izlemeliyim?<\/strong> pip install langchain openai<\/code> komutunu kullanarak Langchain ve OpenAI k\u00fct\u00fcphanelerini kurabilirsiniz.
\n* Hangi LLM'leri kullanabilirim?<\/strong> Langchain, OpenAI, Hugging Face ve benzeri bir\u00e7ok LLM sa\u011flay\u0131c\u0131s\u0131yla uyumludur.
\n* Vekt\u00f6r veritabanlar\u0131 neden \u00f6nemlidir?<\/strong> Vekt\u00f6r veritabanlar\u0131, b\u00fcy\u00fck miktarda veri aras\u0131nda h\u0131zl\u0131 ve etkili bir \u015fekilde benzerlik arama i\u015flemi yapman\u0131za olanak tan\u0131r.
\n* Ba\u011flam y\u00f6netimi neden \u00f6nemlidir?<\/strong> Ba\u011flam y\u00f6netimi, sohbet botunun \u00f6nceki konu\u015fmalar\u0131 hat\u0131rlamas\u0131n\u0131 ve buna g\u00f6re yan\u0131t vermesini sa\u011flar, daha do\u011fal ve ak\u0131c\u0131 bir etkile\u015fim sunar.
\n* Langchain'in \u00fccretlendirmesi nas\u0131l i\u015fliyor?<\/strong> Langchain \u00fccretsiz, a\u00e7\u0131k kaynakl\u0131 bir k\u00fct\u00fcphanedir. Ancak, kulland\u0131\u011f\u0131n\u0131z LLM sa\u011flay\u0131c\u0131s\u0131n\u0131n \u00fccretlendirmesini dikkate almal\u0131s\u0131n\u0131z (\u00f6rne\u011fin OpenAI).<\/p>\nYazar: Fatih Soysal<\/p>\n","protected":false},"excerpt":{"rendered":"Langchain ile Ba\u011flam\u0131 Koruyan Sohbet Botu Geli\u015ftirme G\u00fcn\u00fcm\u00fczde artan veri hacmi ve karma\u015f\u0131k kullan\u0131c\u0131 etkile\u015fimleri, basit sohbet botlar\u0131ndan…","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-28439","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n
Langchain ile Ba\u011flam\u0131 Koruyan Sohbet Botu Geli\u015ftirme<\/title>\n<meta name=\"description\" content=\"G\u00fcn\u00fcm\u00fczde artan veri hacmi ve karma\u015f\u0131k kullan\u0131c\u0131 etkile\u015fimleri, basit sohbet botlar\u0131ndan \u00e7ok daha fazlas\u0131n\u0131 gerektirir. Kullan\u0131c\u0131yla olan etkile\u015fim boyunca ba\u011flam\u0131 hat\u0131rlayan, \u00f6nceki konu\u015fmalar\u0131 anlayan ve buna g\u00f6re yan\u0131t veren bir sohbet botu geli\u015ftirmek, hem kullan\u0131c\u0131 deneyimini iyile\u015ftirir hem de daha ak\u0131ll\u0131 uygulamalar olu\u015fturmaya olanak tan\u0131r. Bu makalede, Python tabanl\u0131 g\u00fc\u00e7l\u00fc bir k\u00fct\u00fcphane olan Langchain kullanarak ba\u011flam\u0131 koruyan bir sohbet botu nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 ad\u0131m ad\u0131m ele alaca\u011f\u0131z. Langchain'in sundu\u011fu ara\u00e7lar ve teknikler sayesinde, karma\u015f\u0131k sohbet senaryolar\u0131n\u0131 bile kolayca y\u00f6netebileceksiniz.\" \/>\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-ile-baglami-koruyan-sohbet-botu-gelistirme\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Langchain ile Ba\u011flam\u0131 Koruyan Sohbet Botu Geli\u015ftirme\" \/>\n<meta property=\"og:description\" content=\"G\u00fcn\u00fcm\u00fczde artan veri hacmi ve karma\u015f\u0131k kullan\u0131c\u0131 etkile\u015fimleri, basit sohbet botlar\u0131ndan \u00e7ok daha fazlas\u0131n\u0131 gerektirir. Kullan\u0131c\u0131yla olan etkile\u015fim boyunca ba\u011flam\u0131 hat\u0131rlayan, \u00f6nceki konu\u015fmalar\u0131 anlayan ve buna g\u00f6re yan\u0131t veren bir sohbet botu geli\u015ftirmek, hem kullan\u0131c\u0131 deneyimini iyile\u015ftirir hem de daha ak\u0131ll\u0131 uygulamalar olu\u015fturmaya olanak tan\u0131r. Bu makalede, Python tabanl\u0131 g\u00fc\u00e7l\u00fc bir k\u00fct\u00fcphane olan Langchain kullanarak ba\u011flam\u0131 koruyan bir sohbet botu nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 ad\u0131m ad\u0131m ele alaca\u011f\u0131z. 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