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LangChain 4 Beginners

Your first step into the GenAI world

Raja CSP Raman

LangChain 4 Beginners teaches you how to build real Generative AI applications by treating LLMs as stateless components and RAG as a system architecture—not magic. Learn how embeddings, vector search, and retrieval pipelines actually work, and build your first production-ready RAG application step by step.

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About

About

About the Book

LangChain 4 Beginners is a practical, systems-first introduction to building real-world Generative AI applications using LangChain.

This book does not treat large language models as intelligent entities or LangChain as a magic framework. Instead, it approaches modern GenAI the way experienced engineers do: as a collection of stateless components that must be carefully orchestrated to produce reliable behavior.

You will learn how to design, build, and reason about LLM-powered systems by understanding why each architectural piece exists—rather than blindly assembling prompts and APIs.

What You Will Learn

By the end of this book, you will understand:

  • How large language models actually work—and why they must be treated as stateless engines
  • Why prompt engineering alone is insufficient for production systems
  • How embeddings and vector search enable semantic retrieval
  • How to load, chunk, embed, and index real documents correctly
  • How Retrieval-Augmented Generation (RAG) works as an architecture pattern
  • How to build your first end-to-end RAG application using LangChain
  • Why hallucinations, context loss, and “AI mistakes” are system design failures
  • How advanced RAG patterns (modular, agentic, graph-based, self-RAG) differ and when to use them

The focus is on architecture, data flow, and failure modes, not hype.

Who This Book Is For

This book is ideal for:

  • Beginners who want to move beyond toy LLM demos
  • Software engineers new to LangChain or Generative AI
  • Developers building internal tools, assistants, or document QA systems
  • Practitioners who want to understand RAG deeply, not just use it

No prior LangChain experience is required. Basic Python familiarity is sufficient.

What This Book Is Not

  • Not a prompt-engineering cookbook
  • Not a collection of copy-paste demos
  • Not an “AI agents will replace everything” narrative

This book emphasizes clear mental models, explicit architecture, and practical engineering tradeoffs.

How the Book Is Structured

The book progresses from fundamentals to complete systems:

  1. Core concepts of LLM behavior and limitations
  2. Loading, chunking, embedding, and vector search
  3. Building your first Retrieval-Augmented Generation (RAG) application
  4. Exploring multiple RAG architectures used in production systems

Each chapter builds on the previous one, with examples designed to clarify structure rather than obscure it.

Why This Book Exists

Most failures in Generative AI are not model failures—they are system failures.

This book exists to help you design systems that:

  • Retrieve the right context
  • Control model behavior
  • Scale beyond a single prompt
  • Fail predictably instead of mysteriously

If you want to build GenAI applications that behave reliably in the real world, this book is for you.

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Author

About the Author

Raja CSP Raman

Raja CSP Raman is a Generative AI systems engineer, architect, and startup advisor focused on building production-grade LLM applications. His work centers on treating large language models as stateless components within well-designed systems, emphasizing retrieval quality, system architecture, evaluation, and failure modes over model hype.

He has designed and reviewed real-world GenAI systems spanning RAG architectures, vector search optimization, agent workflows, and multi-modal reasoning, with a strong bias toward practical constraints such as latency, cost, observability, and reliability. Raja CSP actively mentors founders and engineering teams on how to move from proof-of-concept demos to deployable, maintainable AI products.

This book reflects his engineering philosophy: explain why before how, treat hallucinations as system failures rather than model quirks, and prioritize architecture, data flow, and evaluation as first-class concerns. The content is written for engineers who want to understand how modern LLM systems actually work in production—without marketing gloss, abstractions without grounding, or academic detours.

Contents

Table of Contents

Introduction

  1. Who This Book Is For
  2. What This Book Is (and Is Not)
  3. What This Book Is Not

Understanding the LangChain Landscape

  1. What Problems LangChain Solves
  2. What LangChain Is Not
  3. Simple Mental Diagrams
  4. Beginner Checklist
  5. 1. Python Readiness Checklist
  6. LangChain vs Raw API Usage
  7. Real-World Examples
  8. Evolution of LangChain Architecture
  9. Core Building Blocks Overview
  10. When to Use (and Not Use) LangChain
  11. Summary
  12. Key Takeaway
  13. What Comes Next

Project Setup & First Execution

  1. Python Environment Setup
  2. Dependency Management
  3. Secure API Key Handling
  4. First LLM Call
  5. Debugging Common Beginner Errors
  6. Summary

Prompting Fundamentals

  1. Anatomy of a Prompt
  2. Instruction vs Context vs Input
  3. Prompt Variables
  4. Few-Shot Learning Basics
  5. Prompt Hygiene for Beginners
  6. FewShotPromptTemplate
  7. Summary

Prompt Templates in Practice

  1. Reusable Prompt Blueprints
  2. Dynamic Prompt Inputs
  3. Conditional Prompt Sections
  4. Prompt Composition
  5. Managing Prompt Changes Safely
  6. LangChain Hub Usage
  7. Personalized prompts
  8. Personalized Map Prompt for Theme Extraction
  9. Summary

Structured Outputs Made Simple

  1. Why Structure Matters
  2. Typed Outputs
  3. JSON & Schema-Based Responses
  4. Handling Invalid Model Outputs
  5. Beginner-Friendly Validation Loops
  6. Pydantic output parser
  7. CommaSeparatedListOutputParser
  8. StructuredOutputParser
  9. JSON output parser
  10. Data frame output parser
  11. Date format output parser
  12. EnumOutputParser
  13. OutputFixingParser
  14. Summary

Working with Language Models

  1. Chat Models vs Completion Models
  2. Model Selection Basics
  3. Model Serialization-Save and Call
  4. Token Usage Awareness
  5. Streaming Responses
  6. Switching Providers Safely
  7. LLM models available
  8. Google Generative AI
  9. HuggingFace Endpoints
  10. HuggingFace Local
  11. HuggingFace Pipeline
  12. Ollama
  13. Summary

Conversational Context & Memory

  1. What “Memory” Means in LLM Apps
  2. Short-Term Conversation Context
  3. Windowed Context Strategies
  4. Summarization for Long Chats
  5. Persisting Conversations
  6. ConversationBufferMemory
  7. ConversationBufferWindowMemory
  8. TokenBufferMemory
  9. Conversation SummaryMemory
  10. Memory + LCEL
  11. Save chat to SQLite
  12. RunnableWithMessageHistory
  13. Summary

Loading External Data

  1. Text & Document Inputs
  2. PDFs and Office Files
  3. Web Content Basics
  4. Metadata Introduction
  5. Loader Failure Handling
  6. Structured document
  7. PDF
  8. CSV
  9. Excel
  10. Word
  11. PowerPoint
  12. WebBaseLoader
  13. JSON
  14. TextLoader
  15. Summary

Chunking & Text Preparation

  1. Why Chunking Exists
  2. Fixed vs Recursive Chunking
  3. Token-Aware Splitting
  4. Chunk Metadata
  5. Beginner Chunking Pitfalls
  6. Character splitter
  7. Recursive splitter
  8. Token splitter
  9. Semantic chunker
  10. Summary

Embeddings & Vector Search Basics

  1. What Embeddings Represent
  2. Generating Embeddings
  3. Simple Vector Stores
  4. Similarity Search
  5. First Retrieval Workflow
  6. GPT4ALL embedding
  7. OpenAIEmbeddings
  8. HuggingFace embeddings
  9. Ollama embeddings
  10. FAISS
  11. Pinecone
  12. Chroma
  13. Summary

Your First RAG Application

  1. What RAG Is
  2. RAG Architecture Overview
  3. Context Injection
  4. Simple QA over Documents
  5. RAG Limitations
  6. Summary

Various RAG

  1. Naive RAG
  2. Advanced RAG
  3. Modular RAG
  4. Agentic RAG
  5. Graph RAG
  6. Self-RAG
  7. Summary

Book Summary

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