61 lines
2 KiB
MySQL
61 lines
2 KiB
MySQL
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CREATE TABLE feeds (
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id TEXT PRIMARY KEY,
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url TEXT NOT NULL UNIQUE,
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title TEXT NOT NULL,
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last_fetched_at TEXT
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);
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CREATE TABLE articles (
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id TEXT PRIMARY KEY,
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feed_id TEXT NOT NULL REFERENCES feeds(id),
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url TEXT NOT NULL UNIQUE,
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title TEXT NOT NULL,
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summary TEXT NOT NULL,
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content TEXT,
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published_at TEXT,
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fetched_at TEXT NOT NULL,
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topics TEXT NOT NULL DEFAULT '[]', -- JSON array of strings
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embedding TEXT, -- JSON array of floats
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embedding_score REAL,
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llm_score REAL,
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llm_rationale TEXT,
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final_score REAL,
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estimated_read_seconds INTEGER
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);
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CREATE INDEX idx_articles_feed_id ON articles(feed_id);
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CREATE INDEX idx_articles_final_score ON articles(final_score);
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-- Append-only interaction log. Affinities and any future scoring model are
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-- derived from this; it is never mutated except to backfill dwell_seconds
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-- once known.
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CREATE TABLE reading_events (
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id TEXT PRIMARY KEY,
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article_id TEXT NOT NULL REFERENCES articles(id),
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occurred_at TEXT NOT NULL,
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outcome TEXT NOT NULL, -- 'impression' | 'opened' | 'starred' | 'dismissed'
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dwell_seconds INTEGER
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);
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CREATE INDEX idx_reading_events_article_id ON reading_events(article_id);
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-- Single-row-per-user table (multi-user support can add a user_id column
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-- later); holds the current TopicAffinities snapshot as JSON so it doesn't
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-- need to be recomputed from the full event log on every request. Rebuild
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-- from reading_events at any time if the scoring model changes.
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CREATE TABLE topic_affinities (
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user_id TEXT PRIMARY KEY DEFAULT 'default',
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affinities TEXT NOT NULL DEFAULT '{}',
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updated_at TEXT NOT NULL
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);
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-- Free-text preferences the user writes explicitly (e.g. "I care about
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-- distributed systems and Rust internals, not general AI news"), embedded
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-- once and used as the anchor for the embedding-similarity stage.
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CREATE TABLE preferences (
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user_id TEXT PRIMARY KEY DEFAULT 'default',
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description TEXT NOT NULL DEFAULT '',
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embedding TEXT,
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updated_at TEXT NOT NULL
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);
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