Generative AI with Python: The Developer's Guide to Pretrained LLMs, Vector Databases, Retrieval Augmented Generation, and Agentic Systems (Rheinwerk Computing)

Gollnick, Bert

ISBN 10: 1493226908 / ISBN 13: 9781493226900

Published by Rheinwerk Computing, 2025

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Bibliographic Details

• Title: Generative AI with Python: The Developer's Guide to Pretrained LLMs, Vector Databases, Retrieval Augmented Generation, and Agentic Systems (Rheinwerk Computing)
• Publisher: Rheinwerk Computing
• Publication Date: 2025
• Language: English
• ISBN 10: 1493226908
• ISBN 13: 9781493226900
• Binding: paperback
• Edition: New.
• Condition: New

About this title

Synopsis

Your guide to generative AI with Python is here! Start with an introduction to generative AI, NLP models, LLMs, and LMMs—and then dive into pretrained models with Hugging Face. Work with LLMs using Python with the help of tools like OpenAI and LangChain. Get step-by-step instructions for working with vector databases and using retrieval-augmented generation. With information on agentic systems and AI application deployment, this guide gives you all you need to become an AI master!

• Work with pretrained LLM and NLP models on Hugging Face and LangChain
• Create vector databases and implement retrieval-augmented generation
• Add an agentic system using frameworks such as crewAI and AutoGen

Large Language Models
Set up LLMs and then learn how to apply your models using Python. Walk through the available tools: OpenAI, Meta’s Llama model family, Mistral models via Groq, and open-source LLMs. Work with prompt templates, chains, and more.

Vector Databases
Create and use vector databases to store and query large collections of documents. Master all aspects of the pipeline, from importing a raw document, to processing it, to storing it in a vector database.

Retrieval Augmented Generation
Leverage large-scale pretrained language models and external knowledge sources with retrieval-augmented generation. Retrieve relevant information from large corpora, integrate it into the generation process, and evaluate the quality and diversity of the generated texts.

Agentic Systems
Use AI models to build agents that act autonomously to achieve their goals. Discover the different Python packages for this task: crewAI, AutoGen, and LangChain.

• Natural language processing (NLP) models
• Large language models (LLMs)
• Pretrained models
• Prompt engineering
• Vector databases
• Retrieval-augmented generation (RAG)
• Agentic systems
• OpenAI
• LangChain
• Hugging Face
• crewAI
• AutoGen

About the Author

Bert Gollnick is a senior data scientist who specializes in renewable energies. For many years, he has taught courses about data science and machine learning, and more recently, about generative AI and natural language processing. Bert studied aeronautics at the Technical University of Berlin and economics at the University of Hagen. His main areas of interest are machine learning and data science.