Langgraph Crash Course

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Langgraph Crash Course

Langgraph Crash Course written by GREG. LIM and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-28 with Computers categories.

In this book, we take you on a fun, hands-on and pragmatic journey to learning LangGraph. You'll start building your first graph within minutes. Every chapter is written in a bite-sized manner and straight to the point as I don't want to waste your time (and most certainly mine) on the content you don't need. In the course of this book, we will cover: - Introduction and Type Annotation - Elements - Hello World Graph - Multiple Inputs Graph - Conditional Graph - Simple AI Agent Bot - Agent with Conversation History - Reasoning and Acting (ReAct) Agent - Task List Assistant Agent - RAG Agent The goal of this book is to teach you LangGraph in a manageable way without overwhelming you. We focus only on the essentials and cover the material in a hands-on practice manner for you to code along. Working Through This Book This book is purposely broken down into short chapters where the development process of each chapter will center on different essential topics. The book takes a practical hands on approach to learning through practice. You learn best when you code along with the examples in the book.

Build Ai Agents With Langgraph And Python

Build Ai Agents With Langgraph And Python written by Gregory Lane and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-15 with Computers categories.

Build AI Agents with LangGraph and Python is a hands-on crash course that teaches you how to design, implement, and deploy intelligent, memory-enabled agents using LangGraph and Python. If you're a developer, product builder, or curious technologist who wants to move beyond prompts and build real LLM-powered apps, this book gives you practical patterns, working code, and production-minded advice. Inside you'll find step-by-step examples that take you from a minimal hello-world graph to agents with retrieval-augmented generation (RAG), short- and long-term memory, decisioning, and safe tool use. Learn how to model agents as graphs, define typed contracts with Pydantic, integrate vector search, and connect to modern LLM providers - all while keeping systems observable, testable, and auditable. This book is built for fast learning: compact chapters, copy-ready code, and exercises that give you working results in hours, not months. Whether you're prototyping a support triage bot, a research assistant, or an automation playbook, you'll finish with reusable patterns and a clear roadmap for production. Includes a companion GitHub repo with runnable examples. Stop guessing with prompts - build reproducible, maintainable agents. Start building today.

Building Agentic Ai

Building Agentic Ai written by Sinan Ozdemir and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-08 with Computers categories.

Transform Your Business with Intelligent AI to Drive Outcomes Building reactive AI applications and chatbots is no longer enough. The competitive advantage belongs to those who can build AI that can respond, reason, plan, and execute. Building Agentic AI: Workflows, Fine-Tuning, Optimization, and Deployment takes you beyond basic chatbots to create fully functional, autonomous agents that automate real workflows, enhance human decision-making, and drive measurable business outcomes across high-impact domains like customer support, finance, and research. Whether you're a developer deploying your first model, a data scientist exploring multi-agent systems and distilled LLMs, or a product manager integrating AI workflows and embedding models, this practical handbook provides tried and tested blueprints for building production-ready systems. Harness the power of reasoning models for applications like computer use, multimodal systems to work with all kinds of data, and fine-tuning techniques to get the most out of AI. Learn to test, monitor, and optimize agentic systems to keep them reliable and cost-effective at enterprise scale. Master the complete agentic AI pipeline Design adaptive AI agents with memory, tool use, and collaborative reasoning capabilities Build robust RAG workflows using embeddings, vector databases, and LangGraph state management Implement comprehensive evaluation frameworks beyond accuracy, including precision, recall, and latency metrics Deploy multimodal AI systems that seamlessly integrate text, vision, audio, and code generation Optimize models for production through fine-tuning, quantization, and speculative decoding techniques Navigate the bleeding edge of reasoning LLMs and computer-use capabilities Balance cost, speed, accuracy, and privacy in real-world deployment scenarios Create hybrid architectures that combine multiple agents for complex enterprise applications Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

The Langgraph Langchain Developer S Guide

The Langgraph Langchain Developer S Guide written by Edward R DeForest and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-13 with Computers categories.

Unlock the power of AI and take your projects to the next level with "LangGraph & LangChain: Mastering Next-Generation AI Development"-the ultimate guide to building intelligent systems using cutting-edge NLP technologies. Designed for developers, entrepreneurs, and AI enthusiasts, this book simplifies the complexities of LangChain and LangGraph, providing hands-on insights and practical examples to transform how you create and deploy AI-powered applications. Whether you're crafting Generative AI models, developing real-time chatbots, or managing knowledge graphs, this book offers everything you need to harness the capabilities of LangGraph and LangChain. From learning prompt engineering to mastering LLM integration, each chapter builds your expertise, empowering you to design systems that adapt, scale, and deliver exceptional performance. Explore key topics such as: LangGraph for Beginners: Start with the fundamentals of knowledge graphs and learn how to structure and query data effectively. LangChain in Your Pocket: Discover the versatility of LangChain for conversational AI, generative applications, and beyond. Generative AI on Google Cloud: Learn how to deploy LangChain-powered AI systems on scalable cloud infrastructure. Hands-On Mastery: Follow step-by-step guides to build real-world applications, from prompt-driven tools to advanced multimodal systems. This crash course is packed with expert insights, personal anecdotes, and actionable strategies to keep you ahead in the competitive field of AI. Whether you're a beginner with "CrewAI LangGraph for Beginners" or an experienced developer diving into "Generative AI with LangChain", this book adapts to your needs. Transform your career with a learning experience tailored to the modern AI ecosystem. Grab your copy now and start mastering LangChain, LangGraph, and the technologies shaping the future of AI development!

Dspy Crash Course 2026

Dspy Crash Course 2026 written by Freddie Becka and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-15 with Computers categories.

DSPy Crash Course 2026 A Fast-Track Guide to Agentic AI, Declarative LLM Workflows, RAG Optimization, Multimodal Pipelines, Self-Improving Agents, and Production-Ready Python AI Applications Modern AI systems are no longer built with prompts alone. They are engineered. DSPy Crash Course 2026 is a practical, fast-track guide for developers, AI engineers, and data scientists who want to move beyond prompt engineering and build structured, agentic, and self-improving AI systems using the DSPy framework. This book teaches you how to design declarative LLM workflows, construct agentic AI architectures, and deploy production-ready Python applications that scale reliably in real-world environments. Every concept is grounded in engineering principles, real implementations, and practical design patterns used by modern AI teams. Inside this crash course, you will learn how to: Build agentic AI systems with predictable behavior and modular design Use DSPy's declarative programming model to replace fragile prompt chains Design and optimize Retrieval-Augmented Generation (RAG) pipelines Implement self-improving agents using evaluation-driven optimization Create multimodal AI pipelines that combine text, tools, and structured data Integrate DSPy with Python, LangChain, and LangGraph for real applications Debug, evaluate, and refine complex AI workflows with confidence Deploy production-ready AI systems built for performance and reliability Unlike fragmented tutorials or surface-level introductions, this book provides a clear, structured path from fundamentals to advanced implementation. Each chapter builds on the last, combining theory, architecture, and full working code examples to ensure deep understanding and practical mastery. Whether you are an experienced developer upgrading your AI stack, an AI engineer building agentic systems, or a technical professional preparing for the next generation of LLM development, DSPy Crash Course 2026 gives you the fastest and most reliable path to mastering DSPy and modern AI engineering. If you want to build real AI agents, not brittle demos, this is the book that shows you how.

Vector Database Rag Crash Course

Vector Database Rag Crash Course written by Freddie Becka and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-17 with Computers categories.

Modern AI systems no longer succeed on model size alone. The real competitive advantage now lies in how effectively systems retrieve, reason over, and ground knowledge at scale. Vector Database & RAG Crash Course is a practical, engineering-first guide to building production-ready retrieval-augmented generation (RAG) systems powered by modern vector databases, multimodal embeddings, and agent memory architectures. Written for experienced developers, ML engineers, and AI practitioners, this book goes beyond theory to show how real-world AI systems are designed, optimized, secured, and deployed. Unlike introductory books that stop at basic semantic search, this crash course dives deep into how retrieval actually works in production - from embedding generation and index design to query routing, hybrid search, observability, and enterprise-scale performance tuning. You will learn not just how to build RAG pipelines, but why certain architectural choices succeed or fail under real workloads. Throughout the book, you will implement complete, runnable Python examples and progressively evolve them into robust, scalable retrieval systems. You will explore vector database internals, compare index types and storage models, integrate RAG with LangChain and LangGraph, and design persistent memory layers for intelligent agents. Advanced chapters cover multimodal retrieval, compression and reranking strategies, hybrid vector-keyword search, and techniques for scaling RAG across distributed environments. Security, compliance, and reliability are treated as first-class concerns. You will learn how to defend against prompt injection, prevent data leakage, enforce governance constraints, and monitor retrieval quality in production. Each chapter connects technical implementation with real-world use cases, including enterprise assistants, automated research systems, and long-running agent workflows. Whether you are building internal knowledge systems, customer-facing AI products, or autonomous agent platforms, this book equips you with the engineering patterns, architectural insight, and practical experience needed to design retrieval systems that actually work at scale. What You Will Learn How vector databases power modern semantic search and RAG systems Embeddings, indexing strategies, and performance trade-offs Designing end-to-end RAG pipelines for reliability and accuracy Building multimodal retrieval systems across text, images, and more Implementing persistent agent memory with vector stores Advanced RAG techniques including reranking, compression, and hybrid search Debugging, testing, and optimizing retrieval pipelines in production Securing RAG systems against injection, leakage, and compliance risks Scaling retrieval systems for enterprise and distributed environments Who This Book Is For This book is written for software engineers, machine learning engineers, AI researchers, data scientists, and technical architects who already understand Python and modern AI concepts and want a deep, practical understanding of vector databases and retrieval-augmented generation in real production systems.