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Building Conversational Generative AI Apps with Langchain and GPT: Develop End-to-End LLM-powered Conversational AI Apps with Python LangChain GPT and Google Colab - cover

Building Conversational Generative AI Apps with Langchain and GPT: Develop End-to-End LLM-powered Conversational AI Apps with Python LangChain GPT and Google Colab

Mugesh S.

Publisher: Orange Education Pvt Ltd

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Summary

Transform Text into Intelligent Conversations with LangChain and GPT.Key Features● Build AI Chatbots with LangChain, Python and GPT models through hands-on guidance.● Master Advanced Techniques like RAG, document embedding, and LLM fine-tuning.● Deploy and Scale conversational AI systems for real-world applications.Book DescriptionConversational AI Apps are revolutionizing the way we interact with technology, enabling businesses and developers to create smarter, more intuitive applications that engage users in natural, meaningful ways. Building Conversational Generative AI Apps with LangChain and GPT is your ultimate guide to mastering AI-driven conversational systems.Starting with core concepts of generative AI and LLMs, you'll learn to build intelligent chatbots and virtual assistants, while exploring techniques like fine-tuning LLMs, retrieval-augmented generation (RAG), and document embedding.As you progress, you'll dive deeper into advanced topics such as vector databases and multimodal capabilities, enabling you to create highly accurate, context-aware AI agents. The book's step-by-step tutorials ensure that you develop practical skills in deploying and optimizing scalable conversational AI solutions.By the end, you'll be equipped to build AI apps that enhance customer engagement, automate workflows, and scale seamlessly.Unlock the potential of Building Conversational Generative AI Apps with LangChain and GPT and create next-gen AI applications today!What you will learn● Build and deploy AI-driven chatbots using LangChain and GPT models.● Implement advanced techniques like retrieval-augmented generation (RAG) for smarter responses.● Fine-tune LLMs for domain-specific conversational AI applications.● Leverage vector databases for efficient knowledge retrieval and enhanced chatbot performance.● Explore multimodal capabilities and document embedding for better context-aware responses.● Optimize and scale conversational AI systems for large-scale deployments.
Available since: 06/04/2025.
Print length: 430 pages.

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