395fc17092
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) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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84 B
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7 lines
84 B
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__pycache__/
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tinyweb_identity
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index.db
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index.db-shm
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index.db-wal
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models/
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index.hnsw
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