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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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Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
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Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
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Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
Appendix: Further Reading: Index

Freshness and retention

Fresh data and effective retention strategies ensure that your content is relevant and delivered on time. Freshness keeps users engaged with the latest articles, comments, and recommendations. Retention strategies manage the data lifecycle, preserving valuable historical data for analytics while purging obsolete data. This section explores methods for ensuring up-to-date content and efficient data flow.

Real-time updates

The primary concern is to ingest and update new data in real time, making it available across all cloud regions. For the news site, this means new articles and their vector embeddings should be promptly persisted and replicated for global access.

To achieve this with a distributed data model and application, use an ACID transaction to ensure that the article and its content embeddings are written together as a single unit. For an example of creating MongoDB transactions in Python, see https://learn.mongodb.com/learn/course/mongodb-crud...

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