 Название: 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
Автор: Mugesh S.
Издательство: Orange Education Pvt Ltd, AVA
Год: 2025
Страниц: 652
Язык: английский
Формат: epub (true)
Размер: 74.1 MB
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!
Python offers several libraries and frameworks that are commonly used in Conversational AI projects. Here are some basic ones:
• TensorFlow and Keras:
TensorFlow is an open-source deep learning framework developed by Google, while Keras is a high-level neural networks API that runs on top of TensorFlow. Both libraries are widely used for building and training deep learning models, including neural networks for natural language processing tasks such as text classification, sequence labelling, and text generation.
• PyTorch:
PyTorch is another popular deep-learning framework that offers dynamic computation graphs and a flexible architecture for building and training neural networks. It provides tools and modules for natural language processing tasks, along with pre-trained models and utilities for working with text data.
• Transformers (Hugging Face):
Transformers is a state-of-the-art library by Hugging Face that provides easy-to-use APIs for working with transformer-based models, including BERT, GPT, and their variants. It offers pre-trained models for various NLP tasks and allows fine-tuning and customization for specific use cases.
• Dialogflow (formerly API.AI):
Dialogflow is a natural language understanding platform developed by Google that allows developers to build conversational interfaces, including chatbots and voice applications. It provides tools for designing conversational flows, defining intents and entities, and integrating with various messaging platforms.
• Rasa:
Rasa is an open-source conversational AI platform that enables developers to build, deploy, and manage conversational agents. It provides tools for natural language understanding, dialogue management, and integration with messaging channels. Rasa is known for its flexibility, scalability, and support for building context-aware conversational experiences.
• LangChain
LangChain is a framework that simplifies building powerful and flexible conversational AI systems. It allows seamless integration of language models with external APIs, databases, and memory management, enabling richer and more context-aware conversations.
These libraries and frameworks offer a range of functionalities for developing Conversational AI applications to building and training sophisticated deep learning models for dialogue generation and understanding. Depending on the specific requirements and complexity of your project, you can choose the most suitable tools and libraries to meet your needs.
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.
Who is this book for?
This book is for developers, data scientists, and AI enthusiasts eager to build conversational applications using LangChain and GPT models. While a basic understanding of Python and Machine Learning concepts is beneficial, the book offers step-by-step guidance, making it accessible to both beginners and experienced practitioners.
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
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