Airflow for RAG based GenAI application

https://academy.astronomer.io/introduction-to-genai-with-apache-airflow

At the end of this module, you'll be able to:

• Explain what GenAI is, the challenges associated with GenAI pipelines, and how Airflow addresses those challenges.
• Identify the high-level structural components of a RAG content generation application.
• Use the TaskFlow API to write Airflow DAGs in a pythonic way for the RAG pipeline.
• Use the Airflow Weaviate provider to interact with your Weaviate instance for the RAG pipeline.
• Employ Airflow branching to allow for conditional execution of Airflow tasks for the RAG pipeline.
• Use LangChain to chunk extracted text from local files for the RAG pipeline.
• Use dynamic task mapping to create DAGs that can adapt to changing input at runtime for the RAG pipeline.