13 Project: Keeping family traditions alive with Airflow and Generative AI
This chapter covers
- The concept of Retrieval-Augmented Generation (RAG).
- Implementing Airflow tasks to populate a vector database with your content.
- Retrieving relevant documents from a vector database using vector similarity search.
- Using a large language model (LLM) to generate content based on your knowledge base.
In recent years, the Generative AI (GenAI) revolution has reshaped the way we create text, audio, and image-related content. GenAI systems have emerged as powerful tools capable of generating coherent, contextually relevant text that closely mimics human writing, opening new possibilities across various sectors, from marketing and copywriting to education and customer service.
As we navigate this new era, the demand for high-quality, curated data has never been greater. Organizations and individuals alike are recognizing the importance of preparing, organizing, and providing access to their data pipelines to fuel GenAI applications.
In this chapter, we will explore Apache Airflow’s role in the GenAI landscape. Airflow enables the orchestration of GenAI data pipelines by automating the processes involved in data preparation, empowering users to harness GenAI's full capabilities.