{"product_id":"9781835467992","title":"Building Natural Language and LLM Pipelines : Build production-grade RAG, tool contracts, and context engineering with Haystack and LangGraph","description":"\u003cp\u003eStop LLM applications from breaking in production. Build deterministic pipelines, enforce strict tool contracts, engineer high-signal context for RAG, and orchestrate resilient multi-agent workflows using two foundational frameworks: Haystack for pipelines and LangGraph for low-level agent orchestration.\u003c\/p\u003e\n\n\u003cp\u003eFree with your book: DRM-free PDF version + access to Packt's next-gen Reader* \u003c\/p\u003e\n\n\u003cp\u003eKey Features\u003c\/p\u003e\n\n\u003cp\u003eDesign reproducible LLM pipelines using typed components and strict tool contracts\n\u003cbr\u003eBuild resilient multi-agent systems with LangGraph and modular microservices\n\u003cbr\u003eEvaluate and monitor pipeline performance with Ragas and Weights \u0026amp; Biases\u003c\/p\u003e\n\n\u003cp\u003eBook DescriptionModern LLM applications often break in production due to brittle pipelines, loose tool definitions, and noisy context. This book shows you how to build production-ready, context-aware systems using Haystack and LangGraph. You'll learn to design deterministic pipelines with strict tool contracts and deploy them as microservices. Through structured context engineering, you'll orchestrate reliable agent workflows and move beyond simple prompt-based interactions. \n\u003cbr\u003eYou'll start by understanding LLM behavior—tokens, embeddings, and transformer models—and see how prompt engineering has evolved into a full context engineering discipline. Then, you'll build retrieval-augmented generation (RAG) pipelines with retrievers, rankers, and custom components using Haystack's graph-based architecture. You'll also create knowledge graphs, synthesize unstructured data, and evaluate system behavior using Ragas and Weights \u0026amp; Biases. In LangGraph, you'll orchestrate agents with supervisor-worker patterns, typed state machines, retries, fallbacks, and safety guardrails. \n\u003cbr\u003eBy the end of the book, you'll have the skills to design scalable, testable LLM pipelines and multi-agent systems that remain robust as the AI ecosystem evolves.\u003c\/p\u003e\n\n\u003cp\u003e*Email sign-up and proof of purchase required\u003c\/p\u003e\n\n\u003cp\u003eWhat you will learn\u003c\/p\u003e\n\n\u003cp\u003eBuild structured retrieval pipelines with Haystack\n\u003cbr\u003eApply context engineering to improve agent performance\n\u003cbr\u003eServe pipelines as LangGraph-compatible microservices\n\u003cbr\u003eUse LangGraph to orchestrate multi-agent workflows\n\u003cbr\u003eDeploy REST APIs using FastAPI and Hayhooks\n\u003cbr\u003eTrack cost and quality with Ragas and Weights \u0026amp; Biases\n\u003cbr\u003eImplement retries, circuit breakers, and observability\n\u003cbr\u003eDesign sovereign agents for high-volume local execution\u003c\/p\u003e\n\n\u003cp\u003eWho this book is forLLM engineers, NLP developers, and data scientists looking to build production-grade pipelines, agentic workflows, or RAG systems. Ideal for tech leads looking to move beyond prototypes to scalable, testable solutions, as well as teams modernizing legacy NLP pipelines into orchestration-ready microservices. Proficiency in Python and familiarity with core NLP concepts are recommended.\u003c\/p\u003e","brand":"Packt Publishing Limited","offers":[{"title":"Default Title","offer_id":48412099084523,"sku":"EB_SC1300_F05-01_SIMS","price":86.13,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0758\/4484\/5803\/files\/9781835467992-1.jpg?v=1784346932","url":"https:\/\/kinokuniya.com.sg\/products\/9781835467992","provider":"Books Kinokuniya Singapore","version":"1.0","type":"link"}