Comments (2)
About data structure
Typo
TBD
Word
TBD
Proximity
TBD
Attributes
To rank documents efficiently, we should index word-positions in layers of range as we do for facet numbers:
each case in each layer corresponds to a list of docids where the word is present in the corresponding range of positions. The unit layer corresponds to a list of docids where the word is present at the corresponding position.
Attribute criterion will use higher layers to pre-rank documents digging deeper in layers with fewer and fewer interesting docids to rank.
Exact
TBD
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Related Issues (20)
- `validate_document_id` function trims the id, but maybe shouldn't HOT 3
- Improve speed of incremental indexing
- Temporarily remove the windows tests HOT 1
- Is there any way to reduce the release executable size? HOT 5
- Reduce the size of the `word_pair_proximity` database HOT 4
- Add missing logging timer to extractors
- Store detected Language per document during indexing HOT 8
- Phrase search containing duplicates
- Enhance word splitting strategy HOT 7
- [docs] fix spelling of `author` in `README.md` HOT 1
- [docs] fix spelling of `author` in `README.md`
- Phrase search containing stop words never retrieve any documents
- Delete empty file HOT 1
- Replace deprecated `set-output` in GitHub actions HOT 7
- `cargo test` fails in main branch HOT 8
- `ubuntu-latest` is a better label for `20.04` HOT 1
- Use pre-compiled binaries for faster CI HOT 1
- Fix ShellCheck errors HOT 3
- Incorrect identification of the primary key when it is not directly set HOT 2
- Benchmark CIs fail
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