Generative AI with Python The Developer’s Guide to Pretrained LLMs, Vector Databases, Retrieval-Augmented Generation, and Agentic Systems

In this book, you'll learn about:

1. 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, Groq, and open-source LLMs. Work with prompt templates, chains, and more.

2. Vector Databases

   Create and use vector databases to store and query large collections of documents. Master all aspects of the pipeline: loading a raw document, processing it, and storing it in your vector database.

3. 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.

4. Agentic Systems

   Use AI models to build agents that act autonomously to achieve their goals. Discover the different frameworks for this task: LangGraph, AG2, CrewAI, OpenAI Agents, and Pydantic AI.