Generative AI with Python

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 AG2

You'll learn about:

• 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.
• 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.
• 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 frameworks for this task: LangGraph, AG2, CrewAI, OpenAI Agents, and Pydantic AI.

Key Highlights:

• 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
• AG2