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Building AI Intensive Python Applications

Building AI Intensive Python Applications

By : Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmond Alake, Shubham Ranjan
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Building AI Intensive Python Applications

Building AI Intensive Python Applications

By: Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmond Alake, Shubham Ranjan

Overview of this book

The era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you’ll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications. The chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You’ll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You’ll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you’ll be able to enhance their performance and relevance. By the end of this book, you’ll be well-equipped to build sophisticated AI applications that deliver real-world value.
Table of Contents (18 chapters)
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3
Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
8
Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
11
Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
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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