Generative AI with Python The Developer’s Guide to Pretrained LLMs, Vector Databases, Retrieval Augmented Generation, and Agentic Systems
ISBN 978-1-4932-2690-0 475 pages, 2025
E-book formats: EPUB, PDF, online
ISBN 978-1-4932-2691-7 475 pages, 2025, Print edition paperback
E-book formats: EPUB, PDF, online
ISBN 978-1-4932-2692-4
- 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
In this book, you'll learn about:
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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.
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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.
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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.
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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.
Highlights include:
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Natural language processing (NLP) models
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Large language models (LLMs)
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Pretrained models
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Prompt engineering
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Vector databases
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Retrieval-augmented generation (RAG)
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Agentic systems
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OpenAI
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LangChain
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Hugging Face
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crewAI
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AutoGen