- Add semantic_search setting to toggle AI-powered search on/off
- Skip embedding generation, hybrid search, and model preloading when disabled
- Use site owner's meta description as snippet instead of heuristic extraction
- Remove _generate_summary() and snippet() - no more generated snippets
- Show reranker/reindex controls grayed out when semantic search is off
- AI dependencies (onnxruntime, hnswlib, etc.) are now fully optional
Previously reindex skipped pages that already had chunks, leaving stale
embeddings in place. It also overwrote good meta description summaries
with auto-generated ones. Now it clears all chunks first so everything
is re-embedded, and only generates summaries for pages missing one.
Implements a three-stage search pipeline:
1. BM25 keyword search via FTS5 with column weights
2. Semantic search via Snowflake arctic-embed-s bi-encoder + HNSW index
3. Optional cross-encoder reranking (on by default, toggleable in settings)
Top 20 results are reranked for precision, next 10 appended from RRF
for coverage, giving 30 total results across 3 pages.
- New embeddings.py with ONNX Runtime inference, text chunking, HNSW
index management, RRF fusion, and cross-encoder reranking
- Meta description extraction for authentic page snippets with centroid
extractive fallback
- Stopword filtering in FTS5 queries to avoid overly strict matching
- /reindex page for batch embedding of existing pages
- Semantic embedding of remote pages during subscription sync
- ~125MB dependency footprint (onnxruntime, tokenizers, hnswlib, numpy)
- Models: 34MB bi-encoder + 22MB cross-encoder (downloaded on first use)
