Building Conversational Generative AI Apps with Langchain and GPT

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 Description Conversational 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. Table of Contents 1. Introduction to Conversational Generative AI 2. Natural Language Processing (NLP) Fundamentals 3. The Building Blocks of Rule-Based Chatbots 4. Statistical Language Models for Text Generation 5. Neural Network Architectures for Conversation 6. The Transformer Architecture Revolution 7. Unveiling ChatGPT and Architectures 8. Langchain Framework for Building Conversational AI 9. Exploring the LLM Landscape beyond GPT 10. The Transformative Impact of Conversational AI 11. Challenges and Opportunities in LLM Development Index About the Authors Mugesh S. is an AI developer at LTIMindtree with a strong passion for leveraging data-driven insights to solve complex challenges and drive business innovation. Building on his engineering background he completed a postgraduate program in Data Science and Engineering along with a Master’s degree in Mathematics focused on Data Science. His expertise spans across Python programming machine learning and artificial intelligence with a deep understanding of both theoretical foundations and practical implementations. With over 8 years of hands-on experience he has worked extensively on time series forecasting optical character recognition (OCR) computer vision natural language processing (NLP) and large-scale SQL/NoSQL projects. His specialization in Generative AI (Gen AI) and Large Language Models (LLMs) has led him to develop innovative AI solutions that enhance business efficiency and automate complex processes.