Initial implementation of manual ngram-based search in MongoDB #993
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datalab
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datalab
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ml-evs/mongo-fts-ngram
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| Run status |
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| Run duration | 07m 50s |
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| Committer | Matthew Evans |
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #993 +/- ##
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+ Coverage 70.18% 70.57% +0.38%
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Files 63 63
Lines 4119 4190 +71
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+ Hits 2891 2957 +66
- Misses 1228 1233 +5
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I think this is ready for review now, though we should not make it the default (yet) until we can test scaling and quality of search results. |
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ml-evs
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Feb 7, 2025
| @@ -206,6 +206,7 @@ def create_app( | |||
| extension.init_app(app) | |||
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| pydatalab.mongo.create_default_indices() | |||
| pydatalab.mongo.create_ngram_item_index() | |||
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This should only be run on one of the API processes, or there should be a lock
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Superseded by #1098, for now |
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Closes #679 -- MongoDB text indexes tokenize using whitespace and punctuation only. This PR investigates whether we can build a manual ngram index, so when searching for refcode
ABCDEF, you get results if you only ask forABC,BCDetc.This is done by making a separate collection called
item_ftsthat is used only for this kind of search, by storingimmutable_id,typeandngramsfor all items, with an index overngrams. Lookup is then done by ngrammifying the query string and doing array lookup and ordering by the number of matches.Will have to fiddle around to see:
Nis, and whether we need to do all N+1-grams up to a fixed range