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 LLMs -- Chapter 5: Mastering Prompts for Creative Content -- Chapter 6: Building 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 & Deploying a ChatGPT Like App Using Streamlit.

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. You will: 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.