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>
75 lines
4.0 KiB
YAML
75 lines
4.0 KiB
YAML
# AI model configurations — each model references a credential and provides
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# the model string, optional openrouter routing (extra_body), and the prompt.
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# ${OUTPUT_FORMAT} is injected by the plugin from its hardcoded schema constant.
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# Override individual models in models.user.yaml.
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models:
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vl_detect_shelves:
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credentials: openrouter
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model: "google/gemini-flash-1.5"
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prompt: |
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# ${OUTPUT_FORMAT} — JSON schema injected by BoundaryDetectorShelvesPlugin
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Look at this photo of a bookcase/shelf unit.
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Count the number of horizontal shelves visible.
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For each interior boundary between adjacent shelves, give its vertical position
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as a fraction 0-1 (0=top of image, 1=bottom). Do NOT include 0 or 1 themselves.
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Return ONLY valid JSON, no explanation:
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${OUTPUT_FORMAT}
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vl_detect_books:
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credentials: openrouter
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model: "google/gemini-flash-1.5"
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prompt: |
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# ${OUTPUT_FORMAT} — JSON schema injected by BoundaryDetectorBooksPlugin
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Look at this shelf photo. Identify every book spine visible left-to-right.
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For each interior boundary between adjacent books, give its horizontal position
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as a fraction 0-1 (0=left edge of image, 1=right edge). Do NOT include 0 or 1.
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Return ONLY valid JSON, no explanation:
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${OUTPUT_FORMAT}
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vl_recognize:
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credentials: openrouter
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model: "google/gemini-flash-1.5"
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prompt: |
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# ${OUTPUT_FORMAT} — JSON schema injected by TextRecognizerPlugin
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Look at this book spine image. Read all visible text exactly as it appears,
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preserving line breaks between distinct text blocks.
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Then use visual cues (font size, position, layout) to identify which part is the title,
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author, publisher, year, and any other notable text.
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Return ONLY valid JSON, no explanation:
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${OUTPUT_FORMAT}
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ai_identify:
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credentials: openrouter
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model: "google/gemini-flash-1.5"
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prompt: |
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# ${RAW_TEXT} — text read from the book spine (multi-line)
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# ${ARCHIVE_RESULTS} — JSON array of candidate records from library archives
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# ${OUTPUT_FORMAT} — JSON schema injected by BookIdentifierPlugin
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Text read from the book spine:
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${RAW_TEXT}
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Archive search results (may be empty):
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${ARCHIVE_RESULTS}
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Your task:
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1. Search the web for this book if needed to find additional information.
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2. Combine the spine text, archive results, and your web search into identification candidates.
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3. Collapse candidates that are clearly the same book (same title + author + year + publisher) into one entry, listing all contributing sources.
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4. Rank candidates by confidence (highest first). Assign a score 0.0-1.0.
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5. Remove any candidates you believe are irrelevant or clearly wrong.
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IMPORTANT — confidence scoring rules:
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- The score must reflect how well the found information matches the spine text and recognized data.
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- If the only available evidence is a title with no author, year, publisher, or corroborating archive results, the score must not exceed 0.5.
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- Base confidence on: quality of spine text match, number of matching fields, archive result corroboration, and completeness of the identified record.
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- A record with title + author + year that appears in multiple archive sources warrants a high score; a record with only a guessed title warrants a low score.
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IMPORTANT — output format rules:
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- The JSON schema below is a format specification only. Do NOT use it as a source of example data.
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- Do NOT return placeholder values such as "The Great Gatsby", "Unknown Author", "Example Publisher", or any other generic example text unless that exact text literally appears on the spine.
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- Return only real books that could plausibly match what is shown on this spine.
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- If you cannot identify the book with reasonable confidence, return an empty array [].
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Return ONLY valid JSON matching the schema below, no explanation:
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${OUTPUT_FORMAT}
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