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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

Data flow

Data flow involves the movement of data through a system, affecting the accuracy, relevance, and speed of the results delivered to consumers, which, in turn, influences their engagement. This section explores design considerations for handling data sources, processing data, prompting LLMs, and embedding models to enrich data using MDN as an example. Figure 6.5 illustrates this flow.

Figure 6.5: Typical data flow in an AI/ML application

Let's us begin with the design for handling data sources. Data can be ingested into MongoDB Atlas either statically (at rest) from files as it is, or dynamically (in motion), allowing for continuous updates, data transformation, and logic execution.

Handling static data sources

The simplest way to import static data is to use mongoimport, which supports JSON, CSV, and TSV formats. It is ideal for initial loads or bulk updates as it can handle large datasets. Moreover, increasing the number of insertion...

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