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Develop Personalized Recommendation Logic with pgvector

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Linked challenge: Agent for Ethical, Personalized Content Recommendation

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Linked challenge
Agent for Ethical, Personalized Content Recommendation

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Design the logic for a `RecommendationAgent` that fetches a user's reading history and preferences, generates embeddings for the current article and user profile, and then queries a PostgreSQL database with pgvector for similar articles. The agent should then generate a list of `RecommendationItem` objects. Describe how you would create and query the pgvector index, and provide conceptual Python code for the agent's core recommendation logic.

Adaptation plan

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

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.