Jay Rabi. Generative AI Apps with Langchain and Python: A Project-Based Approach to Building Real-World LLM Apps

Apress, 2024. — 530 p. — ISBN-13 979-8-8688-0881-4.Генеративные приложения ИИ с Langchain и Python: проектный подход к созданию реальных приложений LLM
Future-proof your programming career through practical projects designed to grasp the intricacies of LangChain's components, from core chains to advanced conversational agents. This hands-on book provides Python developers with the necessary skills to develop real-world Large Language Model (LLM)-based Generative AI applications quickly, regardless of their experience level.Projects throughout the book offer practical LLM solutions for common business issues, such as information overload, internal knowledge access, and enhanced customer communication. Meanwhile, you'll learn how to optimize workflows, enhance embedding efficiency, select between vector stores, and other optimizations relevant to experienced AI users. The emphasis on real-world applications and practical examples will enable you to customize your own projects to address pain points across various industries.Developing LangChain-based Generative AI LLM Apps with Python employs a focused toolkit (LangChain, Pinecone, and Streamlit LLM integration) to practically showcase how Python developers can leverage existing skills to build Generative AI solutions. By addressing tangible challenges, you'll learn-by-be doing, enhancing your career possibilities in today's rapidly evolving landscape.What You Will Learn:
Understand different types of LLMs and how to select the right ones for responsible AI.
Structure effective prompts.
Master LangChain concepts, such as chains, models, memory, and agents.
Apply embeddings effectively for search, content comparison, and understanding similarity.
Setup and integrate Pinecone vector database for indexing, structuring data, and search.
Build Q & A applications for multiple doc formats.
Develop multi-step AI workflow apps using LangChain agents.About the Technical Reviewers
Chapter 1: Introduction to LangChain and LLMs
Chapter 2: Integrating LLM APIs with LangChain
Chapter 3: Building Q&A and Chatbot Apps
Chapter 4: Exploring Large Language Models (LLMs)
Chapter 5: Mastering Prompts for Creative Content
Chapter 6: Building Intelligent Chatbots and Automated Analysis Systems Using Chains
Chapter 7: Building Advanced Q&A and Search Applications Using Retrieval-Augmented Generation (RAG)
Chapter 8: Your First Agent App
Chapter 9: Building Different Types of Agents
Chapter 10: Projects: Building Agent Apps for Common Use Cases
Chapter 11: Building and Deploying a ChatGPT-like App Using Streamlit