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Building AI Intensive Python Applications

You're reading from   Building AI Intensive Python Applications Create intelligent apps with LLMs and vector databases

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781836207252
Length 298 pages
Edition 1st Edition
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Table of Contents (18) Chapters Close

Preface 1. Chapter 1: Getting Started with Generative AI FREE CHAPTER 2. Chapter 2: Building Blocks of Intelligent Applications 3. Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
4. Chapter 3: Large Language Models 5. Chapter 4: Embedding Models 6. Chapter 5: Vector Databases 7. Chapter 6: AI/ML Application Design 8. Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
9. Chapter 7: Useful Frameworks, Libraries, and APIs 10. Chapter 8: Implementing Vector Search in AI Applications 11. Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
12. Chapter 9: LLM Output Evaluation 13. Chapter 10: Refining the Semantic Data Model to Improve Accuracy 14. Chapter 11: Common Failures of Generative AI 15. Chapter 12: Correcting and Optimizing Your Generative AI Application 16. Other Books You May Enjoy Appendix: Further Reading: Index

Technical requirements

This chapter assumes that you have at least beginner-level expertise in Python coding. To follow along with the demos, you’ll need to set up your development environment by completing the following steps:

  1. Install either python@3.9 or python@3.11 on the operating system of your choice.
  2. Set up a Python virtual environment and activate it:
    $ python3 -m venv venv
    $ source venv/bin/activate
  3. You will be using the following packages to develop the demo described in this chapter:
    • pandas: Helps with data preprocessing and handling
    • numpy: Handles numerical data
    • openai: For the embedding model and invoking the LLM
    • pymongo: For the MongoDB Atlas vector store and full-text search
    • s3fs: Allows loading data directly from an S3 bucket
    • langchain_mongodb: Enables vector search in MongoDB Atlas using a LangChain wrapper
    • langchain: Used to build a RAG application
    • langchain-openai: Enables you to interact with OpenAI chat models
    • boto3: Enables you to interact with AWS...
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