"Apache Airflow, a leading open-source orchestration framework, provides the structure and flexibility required to implement complex GenAI workflows with relatively basic Python code. In this tutorial, you'll learn how to use Airflow to orchestrate a basic RAG pipeline that includes embedding book descriptions with the OpenAI API, ingesting the embeddings into a PostgreSQL database with pgvector installed, and querying the database for books that match a user-provided mood."
"Reliable task execution: APIs of AI models often come with transient errors and rate limits. Airflow can automatically retry individual tasks, send notifications in case of issues and handle timeouts. Tool agnosticism: Airflow can connect to any application in your data ecosystem that allows connections through an API. This means you are never locked into one model vendor and can quickly switch individual tasks to the latest and best model and AI tooling."
Productionizing GenAI applications relies on multi-stage pipelines that manage data ingestion, vector embedding creation, vector storage, querying, and response construction. Apache Airflow provides structure and flexibility to implement complex GenAI workflows using familiar Python code. Orchestration can include embedding book descriptions via the OpenAI API, ingesting embeddings into PostgreSQL with pgvector, and querying for books that match user-provided moods. Airflow offers reliable task execution through automatic retries, notifications, and timeout handling. Airflow is tool-agnostic, connecting to any API-capable application and enabling easy swapping of model vendors. High extensibility stems from Python-based pipelines and a broad ecosystem of provider packages.
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