Hands-On Agentic AI with LangChain, RAG, and Ollama:: Build Modular Local AI Agents, Retrieval Pipelines, Tool-Using Workflows, and Production-Ready Applications - Softcover

Inhaltsangabe

Hands-On Agentic AI with LangChain, RAG, and Ollama: Build Modular Local AI Agents, Retrieval Pipelines, Tool-Using Workflows, and Production-Ready Applications

Stop building AI demos that look impressive once but fall apart when the workflow gets real.

If you want to create AI agents that retrieve private knowledge, use tools, follow controlled steps, run locally, and move toward production with confidence, this book gives you a practical build path from setup to deployment.

Many AI agent tutorials stop at prompts and toy examples. This book goes further. You will learn how to build modular local AI agents with LangChain, connect them to Ollama models, create retrieval-augmented generation pipelines, improve search quality, orchestrate workflows with LangGraph, and prepare agentic AI applications for real use.

Inside, you will learn how to:

• Set up Python, Ollama, LangChain, Chroma, FastAPI, and core AI development tools
• Build tool-using agents with structured outputs, retries, and error handling
• Create local RAG pipelines with embeddings, chunking, metadata, and vector stores
• Improve retrieval accuracy with query rewriting, hybrid search, and reranking
• Design controlled agentic RAG workflows with verification rules
• Add testing, tracing, logging, security habits, and production readiness checks
• Deploy a local RAG agent application with FastAPI and Docker

The book is written for Python developers, backend engineers, data engineers, technical founders, students, and AI builders who want practical agentic AI engineering without depending on a dedicated external repository. All core code is included directly in the book.

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