Petr Polezhaev
b94f222c96
- log_thread.py: thread-safe ContextVar bridge so executor threads can log
individual LLM calls and archive searches back to the event loop
- ai_log.py: init_thread_logging(), notify_entity_update(); WS now pushes
entity_update messages when book data changes after any plugin or batch run
- batch.py: replace batch_pending.json with batch_queue SQLite table;
run_batch_consumer() reads queue dynamically so new books can be added
while batch is running; add_to_queue() deduplicates
- migrate.py: fix _migrate_v1 (clear-on-startup bug); add _migrate_v2 for
batch_queue table
- _client.py / archive.py / identification.py: wrap each LLM API call and
archive search with log_thread start/finish entries
- api.py: POST /api/batch returns {already_running, added}; notify_entity_update
after identify pipeline
- models.default.yaml: strengthen ai_identify confidence-scoring instructions;
warn against placeholder data
- detail-render.js: book log entries show clickable ID + spine thumbnail;
book spine/title images open full-screen popup
- events.js: batch-start handles already_running+added; open-img-popup action
- init.js: entity_update WS handler; image popup close listeners
- overlays.css / index.html: full-screen image popup overlay
- eslint.config.js: add new globals; fix no-redeclare/no-unused-vars for
multi-file global architecture; all lint errors resolved
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
"""Database migration functions.
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Each migration is idempotent and safe to run on a database that has already been migrated.
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Run via run_migration() called from app startup after init_db().
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"""
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import sqlite3
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from db import DB_PATH
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def run_migration() -> None:
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"""Apply all pending schema migrations in order.
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Currently applies:
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- v1: Add ai_blocks column to books; clear AI-derived data while preserving user data.
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- v2: Add batch_queue table for persistent batch processing queue.
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Migrations are idempotent — running them on an already-migrated database is a no-op.
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"""
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c = sqlite3.connect(DB_PATH)
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c.row_factory = sqlite3.Row
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c.execute("PRAGMA foreign_keys = ON")
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try:
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_migrate_v1(c)
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_migrate_v2(c)
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c.commit()
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except Exception:
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c.rollback()
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raise
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finally:
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c.close()
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def _migrate_v1(c: sqlite3.Connection) -> None:
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"""Add ai_blocks column and clear stale AI data from all books (first run only).
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- Adds ai_blocks TEXT DEFAULT NULL column if it does not exist.
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- On first run only (when the column is absent): clears raw_text, ai_*, title_confidence,
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analyzed_at, candidates, ai_blocks from all books (these are regenerated by the new pipeline).
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- For user_approved books: copies user fields back to ai_* so that
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compute_status() still returns 'user_approved' after the ai_* clear.
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This migration assumes the database already has the base books schema.
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It is a no-op if ai_blocks already exists.
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"""
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cols = {row["name"] for row in c.execute("PRAGMA table_info(books)")}
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if "ai_blocks" not in cols:
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c.execute("ALTER TABLE books ADD COLUMN ai_blocks TEXT DEFAULT NULL")
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# Clear AI-derived fields only when first adding the column.
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c.execute(
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"UPDATE books SET "
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"raw_text='', ai_title='', ai_author='', ai_year='', ai_isbn='', ai_publisher='', "
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"title_confidence=0, analyzed_at=NULL, candidates=NULL, ai_blocks=NULL"
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)
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# For user_approved books, restore ai_* = user fields so status stays user_approved.
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c.execute(
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"UPDATE books SET "
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"ai_title=title, ai_author=author, ai_year=year, ai_isbn=isbn, ai_publisher=publisher "
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"WHERE identification_status='user_approved'"
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)
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def _migrate_v2(c: sqlite3.Connection) -> None:
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"""Add batch_queue table for persistent batch processing queue.
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Replaces data/batch_pending.json with a DB table so batch state survives
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across restarts alongside all other persistent data.
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"""
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c.execute("CREATE TABLE IF NOT EXISTS batch_queue (" "book_id TEXT PRIMARY KEY," "added_at REAL NOT NULL" ")")
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