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Total Size:
61.4 MB
Info Hash:
7F064436317C8684B5F932D4429B0581A9719849
Added By:
Added:
April 9, 2026, 8:06 a.m.
Stats:
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(Last updated: April 9, 2026, 8:07 a.m.)
| File | Size |
|---|---|
| Funderburk L. Building Natural Language and LLM Pipelines...2025.pdf | 8.8 MB |
| Code.zip | 52.6 MB |
NOTE
SOURCE: Funderburk L. Building Natural Language and LLM Pipelines...2025
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COVER
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MEDIAINFO
Textbook in PDF format Key benefits Design reproducible LLM pipelines using typed components and strict tool contracts Build resilient multi-agent systems with LangGraph and modular microservices Evaluate and monitor pipeline performance with Ragas and Weights & Biases Description 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. *Email sign-up and proof of purchase required Who is this book for? LLM 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. What you will learn Build structured retrieval pipelines with Haystack Apply context engineering to improve agent performance Serve pipelines as LangGraph-compatible microservices Use LangGraph to orchestrate multi-agent workflows Deploy REST APIs using FastAPI and Hayhooks Track cost and quality with Ragas and Weights & Biases Implement retries, circuit breakers, and observability Design sovereign agents for high-volume local execution
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