About this ebook
Calling an API and printing the response is not an application. An application retrieves relevant data before the model ever sees the question. It remembers what was said three messages ago. It decides which tool to call and in what order. It handles failures gracefully, streams responses to users in real time, and logs every step so you can debug what went wrong at 2am when your customer is waiting.
LangChain is the framework that handles all of that plumbing — so you can focus on building the thing that actually matters.
This book is the complete, code-first guide to building production LLM applications with LangChain, from your first chain to a deployed, monitored, evaluated system handling real users.
Inside you will find:
The full LangChain stack — models, prompt templates, output parsers, and LangChain Expression Language, the pipe-based composition syntax that connects every component into clean, streaming-ready pipelines
Document loaders, text splitters, embedding models, and vector stores — the complete data layer for RAG applications that can answer questions about your own documents, databases, and knowledge bases
Complete RAG implementation from scratch — basic retrieval, advanced re-ranking and hybrid search, conversational RAG with memory, and systematic evaluation with LangSmith
LangChain agents and the ReAct pattern — building systems that reason, decide which tools to call, execute actions, and incorporate results into their next step autonomously
LangGraph for multi-agent systems — stateful workflows, conditional branching, parallel agents, and human-in-the-loop approval patterns for production-grade agentic applications
LangSmith for tracing, testing, and evaluation — the observability layer that tells you what your application is actually doing, where it fails, and whether your changes are improvements
Production deployment with FastAPI — streaming endpoints, session management, error handling, and the architecture patterns that scale
Three complete real-world projects: an enterprise document Q&A system, an autonomous research agent, and a customer support automation system — each built with the patterns taught throughout the book
Every chapter includes working Python code you can run immediately — built on the real LangChain stack, not simplified examples.
If you are serious about building LLM applications that work in the real world, this is the book.
By Fatima Ahmad. Available on Google Play Books.
