Returns up to N randomly sampled page chunks (default 50, max 200) with
chunk_id, doc_id, page_number, and text fields. No tier gate — internal
tool endpoint for same-host corpus benchmarking. Returns [] on empty library.
Implements Option A from the issue design: each cloud user gets their own
data directory (DATA_DIR/users/{user_id}/) with separate pagepiper.db,
pagepiper_vecs.db, uploads/, and books/. Local mode is unchanged.
Key changes:
- app/startup.py: extract apply_migrations, reembed_docs,
check_and_rebuild_vec_schema out of main.py (no circular imports)
- app/config.py: add LOCAL_USER_ID constant and user_data_dir() helper
- app/cloud_session.py: extract resolve_authenticated_user(); require_paid_tier
now returns user_id (str) instead of None
- app/deps.py: add UserCtx dataclass (db_path, vec_db_path, data_dir,
watch_dir, bm25) + get_user_ctx dependency; per-user startup guard runs
migrations + vec schema check once per process per user
- app/main.py: _bm25 singleton -> _bm25_map dict keyed by user_id;
add _get_bm25_for(); lifespan only runs startup checks in local mode
- app/api/library.py, search.py, chat.py: thread UserCtx through all
endpoints; remove module-level _mark_bm25_dirty injection pattern
- tests/conftest.py: override get_user_ctx in addition to get_db so all
endpoints get a consistent test UserCtx
Retrieval:
- Add _fetch_adjacent() to retriever: fetches page ± 1 chunks from DB
after ranking so mid-sentence EPUB chunk boundaries don't lose context
- Fix vec DB doc-filter: oversample to top_k*20 before Python filter
instead of post-filtering an already-small global pool (fixes wrong-book
results when searching within a single document)
- top_k default 5 → 10; context per chunk 500 → 1500 chars; citation
snippet 200 → 400 chars
Artifact cleaning:
- Add scripts/text_clean.py: strips ABC Amber LIT Converter watermarks,
processtext.com URLs, bare page numbers, piracy stamps from extracted text
- Wire clean_paragraph() into ingest_pdf.py and new ingest_epub.py
Startup validation:
- _check_vec_schema() at boot: detects embedding dimension mismatch,
deletes stale vec DB, and queues sequential re-embed in background thread
- Sequential _reembed_docs() prevents SQLite lock races on startup re-embed
cf-orch integration:
- Wire CF_ORCH_URL / CF_LICENSE_KEY into LLMRouter backend config so
allocate() fires and keeps the Ollama model warm between requests
Ingestion progress UI:
- GET /api/library/{doc_id}/status now returns vec_count from page_vecs_meta
- DocumentCard.vue polls status every 3 s while processing and shows
two-phase progress: indeterminate animation during extraction,
determinate "Embedding N/M pages" bar once vectors start landing
Other:
- Chat feedback endpoint + thumbs up/down UI (FeedbackButton.vue)
- EPUB ingest script (ingest_epub.py) with heading-based chunking
- migration 002: chat_feedback table
- README.md with setup and feature overview
Implements GET/DELETE /api/library, POST /api/library/{id}/reingest,
POST /api/library/scan, and GET /api/library/{id}/status. Adds FastAPI
app factory with lifespan migrations, BM25 singleton wiring, get_db
dependency, ingest task registry with cf-orch/BackgroundTasks fallback,
and placeholder search/chat routers. All 5 new tests pass (14 total).
