The content discusses setting up Airflow configurations and DAGs for a local environment. By running a script, necessary configurations, logs, plugins, and a standalone administrator password are accumulated in a specific folder. Placing DAGs in the designated directory allows Airflow to locate them automatically. The setup includes creating an agent with Langchain to retrieve and analyze papers from specific categories published in the last 7 days. The LLM AI agent checks the papers based on a predefined prompt template. Results are then written to a CSV file and uploaded to a Google Drive folder. The process is detailed through code snippets and explanations. Finally, the DAG setup is completed, and users can trigger the workflow to start processing algorithms without waiting for the next 7 days. The content provides a comprehensive guide on setting up Airflow for local environments and creating agent and DAG files for efficient workflow management.
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Source link: https://medium.com/@superckid/utilizing-airflow-for-planning-scheduling-executing-and-scaling-ai-agents-c6196775e29f?source=rss——large_language_models-5
in AI Medium
Leveraging Airflow for AI Agents Planning, Scheduling, and Scaling #AirflowAI
Preeti
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