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

Product type Paperback

Published in Sep 2024

Publisher Packt

ISBN-13 9781836207252

Length 298 pages

Edition 1st Edition

Languages

Python

Tools

MongoDB

Concepts

Artificial Intelligence

Authors (9):

Rachelle Palmer

Ben Perlmutter

Ashwin Gangadhar

Nicholas Larew

Sigfrido Narváez

Thomas Rueckstiess

Henry Weller

Richmond Alake

Shubham Ranjan

+5 more

View More author details

Table of Contents (18) Chapters

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

Why subscribe?

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:

Install either python@3.9 or python@3.11 on the operating system of your choice.
Set up a Python virtual environment and activate it:
```
$ python3 -m venv venv
$ source venv/bin/activate
```
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...