AI Agents and Applications, MEAP, Infante R., 2025

AI Agents and Applications, MEAP, Infante R., 2025.
        
   "AI Agents and Applications" is structured to cater both to beginners and seasoned professionals. It offers a comprehensive look into the foundational technologies and advanced techniques in the realm of LLMs. Whether you are new to programming or an experienced developer, you will find the content approachable yet enriching, especially with practical code examples to enhance your engagement.
The insights in this book are drawn from my real-world experiences and continuous learning in a field that evolves almost daily. I cover a range of topics from running open source LLMs locally to advanced RAG techniques and fine-tuning methods. My goal is to provide a resource that I wish had been available to me—an accessible, practical guide that empowers you to navigate and excel in this emerging domain.




LLM-based chatbots.
An LLM-based chatbot acts as an intelligent assistant, enabling ongoing, natural conversations with a language model. Unlike simple question–answer scripts, these systems aim to keep interactions both useful and safe. They rely heavily on prompt design: clear instructions shape the model’s behavior and help prevent irrelevant, inaccurate, or unsafe replies. Modern chat APIs—such as those from OpenAI—support role-based messaging formats (system, user, assistant), which let you define an assistant’s persona and enforce consistent behavior across a conversation.

To improve accuracy, chatbots often pull in factual context from local knowledge sources like vector stores. This lets them blend conversational fluency with domain-specific grounding, so the answers aren’t just smooth but also relevant and reliable.

A key strength of chatbots is conversation memory. By tracking earlier turns, they can keep responses coherent and personalized. This memory is limited by the model’s context window. Larger windows help, but many systems still compress or summarize conversation history to stay within limits—and to manage cost.

Contents.
PART 1: GETTING STARTED WITH LLMS.
1 Introduction to AI agents and applications.
2 Executing prompts programmatically.
PART 2: SUMMARIZATION.
3 Summarizing text using LangChain.
4 Building a research summarization engine.
5 Agentic workflows with LangGraph.
PART 3: Q&A CHATBOTS.
6 RAG fundamentals with Chroma DB.
7 Q&A chatbots with LangChain and LangSmith.
PART 4: ADVANCED RAG.
8 Advanced indexing.
9 Question transformations.
10 Query generation, routing and retrieval post-processing.
PART 5: AI AGENTS.
11 Building tool-based agents with LangGraph.
12 Multi-agent systems.
13 Building and consuming MCP servers.
14 Productionizing AI agents: Memory, guardrails, and beyond.
Appendix A. Trying out LangChain.
Appendix B. Setting up a Jupyter Notebook environment.
Appendix C. Choosing an LLM.
Appendix D. Installing SQLite on Windows.
Appendix E. Open-source LLMs.



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Теги: учебник по программированию :: программирование :: Infante