14 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.
Having high-quality data is paramount to building a good GenAI system or product, as poor input data will inevitably lead to poor results. Fortunately, Airflow can play an important role in ensuring high quality input data by automating the processes involved in data preparation. In this chapter we’ll explore Airflow’s role in building robust GenAI solutions with an example use case involving family recipes.
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.