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Building Natural Language and LLM Pipelines. Build production-grade RAG, tool contracts, and context engineering with Haystack and LangGraph Laura Funderburk

(ebook) (audiobook) (audiobook) Język publikacji: angielski
Building Natural Language and LLM Pipelines. Build production-grade RAG, tool contracts, and context engineering with Haystack and LangGraph Laura Funderburk - okladka książki

Building Natural Language and LLM Pipelines. Build production-grade RAG, tool contracts, and context engineering with Haystack and LangGraph Laura Funderburk - okladka książki

Building Natural Language and LLM Pipelines. Build production-grade RAG, tool contracts, and context engineering with Haystack and LangGraph Laura Funderburk - audiobook MP3

Building Natural Language and LLM Pipelines. Build production-grade RAG, tool contracts, and context engineering with Haystack and LangGraph Laura Funderburk - audiobook CD

Autor:
Laura Funderburk
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
338
Dostępne formaty:
     PDF
     ePub
Modern 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.
You'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 & Biases. In LangGraph, you’ll orchestrate agents with supervisor-worker patterns, typed state machines, retries, fallbacks, and safety guardrails.
By 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.

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O autorze książki

Laura Funderburk works as a Developer Advocate for Ploomber, an organization focused on improving the MLOps lifecycle. As a Developer Advocate, Laura combines her passion for MLOps, SQL, and data engineering, with her love for community outreach. Prior to this, Laura held positions as a Data Scientist and DevOps engineer in a variety of settings, including academia, non-for-profit and private sectors. Laura obtained a Machine Learning Engineering certification from the University of California San Diego, and a Bachelor of Mathematics from Simon Fraser University. Since the introduction of Large Language Models, Laura has dedicated her time to learning how to package, productionize and automate data extraction, processing and retrieval through LLMs and open-source packages, and has found a framework she loves in Haystack. When not immersed in building pipelines and engaging with the open-source community, Laura trains Brazilian Jiu-jitsu.

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