5ded9f1339
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)
8 lines
81 B
Text
8 lines
81 B
Text
requests
|
|
beautifulsoup4
|
|
rns
|
|
onnxruntime
|
|
tokenizers
|
|
hnswlib
|
|
numpy
|
|
huggingface_hub
|