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Orchestrate Content Pipeline with Airflow and Build Streamlit UI

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Agent for Ethical, Personalized Content Recommendation

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Linked challenge
Agent for Ethical, Personalized Content Recommendation

Prompt source

Original prompt text with formatting preserved for inspection.

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Outline an Apache Airflow DAG that orchestrates the content ingestion, summarization (using your `SummarizerAgent`), embedding generation, and updating of the pgvector database. Separately, describe how you would build a simple Streamlit UI to display the generated summaries and personalized recommendations for a given user, ensuring the interface clearly communicates the 'ad-free' nature. Provide a high-level Airflow DAG structure and a conceptual Streamlit Python script.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Preserve the source structure until you know which part of the prompt is actually driving the result quality.

Tune next

Change domain facts, examples, and tool context first before you rewrite the instruction scaffold.

Verify after

Validate one failure mode at a time so prompt changes stay attributable instead of getting noisy.