feat: soft token matching + Spider benchmark eval scaffold#1
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- Add graph2sql/matching.py: stemming-based soft match scorer
Handles plurals (customers→customer), compound words, content + alias matching
Label matches weighted 2x vs content/attribute matches
Pure Python stdlib — no new dependencies
- Update ranking.py to use soft_match_score instead of exact token matching
Fixes the main weakness: "customer" now matches "customers", "customer_id" etc.
- Add benchmarks/spider_eval.py: table recall eval script for Spider dataset
Measures: mean recall@k, perfect recall fraction, zero recall fraction
Usage: python benchmarks/spider_eval.py --spider-dir ./data/spider --k 3
- Add tests/test_matching.py: 14 tests for matching module
ohwise_backend compatibility note:
graph2sql graph format ({"nodes":[...], "edges":[...]}) is identical to
ohwise_backend PersonalizedPageRank.execute() input format — no changes needed
to integrate graph2sql as a drop-in for the internal PPR service.
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Add graph2sql/matching.py: stemming-based soft match scorer Handles plurals (customers→customer), compound words, content + alias matching Label matches weighted 2x vs content/attribute matches Pure Python stdlib — no new dependencies
Update ranking.py to use soft_match_score instead of exact token matching Fixes the main weakness: "customer" now matches "customers", "customer_id" etc.
Add benchmarks/spider_eval.py: table recall eval script for Spider dataset
Measures: mean recall@k, perfect recall fraction, zero recall fraction
Usage: python benchmarks/spider_eval.py --spider-dir ./data/spider --k 3
Add tests/test_matching.py: 14 tests for matching module
ohwise_backend compatibility note:
graph2sql graph format ({"nodes":[...], "edges":[...]}) is identical to ohwise_backend PersonalizedPageRank.execute() input format — no changes needed to integrate graph2sql as a drop-in for the internal PPR service.