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3821: Add normalized and detailed scores to documents returned by a query r=dureuill a=dureuill # Pull Request ## Related issue Fixes #3771 ## What does this PR do? ### User standpoint <details> <summary>Request ranking score</summary> ``` echo '{ "q": "Badman dark knight returns", "showRankingScore": true, "limit": 10, "attributesToRetrieve": ["title"] }' | mieli search -i index-word-count-10-count ``` </details> <details> <summary>Response</summary> ```json { "hits": [ { "title": "Batman: The Dark Knight Returns, Part 1", "_rankingScore": 0.947520325203252 }, { "title": "Batman: The Dark Knight Returns, Part 2", "_rankingScore": 0.947520325203252 }, { "title": "Batman Unmasked: The Psychology of the Dark Knight", "_rankingScore": 0.6657594086021505 }, { "title": "Legends of the Dark Knight: The History of Batman", "_rankingScore": 0.6654905913978495 }, { "title": "Angel and the Badman", "_rankingScore": 0.2196969696969697 }, { "title": "Angel and the Badman", "_rankingScore": 0.2196969696969697 }, { "title": "Batman", "_rankingScore": 0.11553030303030302 }, { "title": "Batman Begins", "_rankingScore": 0.11553030303030302 }, { "title": "Batman Returns", "_rankingScore": 0.11553030303030302 }, { "title": "Batman Forever", "_rankingScore": 0.11553030303030302 } ], "query": "Badman dark knight returns", "processingTimeMs": 12, "limit": 10, "offset": 0, "estimatedTotalHits": 46 } ``` </details> - If adding a `showRankingScore` parameter to the search query, then documents returned by a search now contain an additional field `_rankingScore` that is a float bigger than 0 and lower or equal to 1.0. This field represents the relevancy of the document, relatively to the search query and the settings of the index, with 1.0 meaning "perfect match" and 0 meaning "not matching the query" (Meilisearch should never return documents not matching the query at all). - The `sort` and `geosort` ranking rules do not influence the `_rankingScore`. <details> <summary>Request detailed ranking scores</summary> ``` echo '{ "q": "Badman dark knight returns", "showRankingScoreDetails": true, "limit": 5, "attributesToRetrieve": ["title"] }' | mieli search -i index-word-count-10-count ``` </details> <details> <summary>Response</summary> ```json { "hits": [ { "title": "Batman: The Dark Knight Returns, Part 1", "_rankingScoreDetails": { "words": { "order": 0, "matchingWords": 4, "maxMatchingWords": 4, "score": 1.0 }, "typo": { "order": 1, "typoCount": 1, "maxTypoCount": 4, "score": 0.8 }, "proximity": { "order": 2, "score": 0.9545454545454546 }, "attribute": { "order": 3, "attributes_ranking_order": 1.0, "attributes_query_word_order": 0.926829268292683, "score": 0.926829268292683 }, "exactness": { "order": 4, "matchType": "noExactMatch", "score": 0.26666666666666666 } } }, { "title": "Batman: The Dark Knight Returns, Part 2", "_rankingScoreDetails": { "words": { "order": 0, "matchingWords": 4, "maxMatchingWords": 4, "score": 1.0 }, "typo": { "order": 1, "typoCount": 1, "maxTypoCount": 4, "score": 0.8 }, "proximity": { "order": 2, "score": 0.9545454545454546 }, "attribute": { "order": 3, "attributes_ranking_order": 1.0, "attributes_query_word_order": 0.926829268292683, "score": 0.926829268292683 }, "exactness": { "order": 4, "matchType": "noExactMatch", "score": 0.26666666666666666 } } }, { "title": "Batman Unmasked: The Psychology of the Dark Knight", "_rankingScoreDetails": { "words": { "order": 0, "matchingWords": 3, "maxMatchingWords": 4, "score": 0.75 }, "typo": { "order": 1, "typoCount": 1, "maxTypoCount": 3, "score": 0.75 }, "proximity": { "order": 2, "score": 0.6666666666666666 }, "attribute": { "order": 3, "attributes_ranking_order": 1.0, "attributes_query_word_order": 0.8064516129032258, "score": 0.8064516129032258 }, "exactness": { "order": 4, "matchType": "noExactMatch", "score": 0.25 } } }, { "title": "Legends of the Dark Knight: The History of Batman", "_rankingScoreDetails": { "words": { "order": 0, "matchingWords": 3, "maxMatchingWords": 4, "score": 0.75 }, "typo": { "order": 1, "typoCount": 1, "maxTypoCount": 3, "score": 0.75 }, "proximity": { "order": 2, "score": 0.6666666666666666 }, "attribute": { "order": 3, "attributes_ranking_order": 1.0, "attributes_query_word_order": 0.7419354838709677, "score": 0.7419354838709677 }, "exactness": { "order": 4, "matchType": "noExactMatch", "score": 0.25 } } }, { "title": "Angel and the Badman", "_rankingScoreDetails": { "words": { "order": 0, "matchingWords": 1, "maxMatchingWords": 4, "score": 0.25 }, "typo": { "order": 1, "typoCount": 0, "maxTypoCount": 1, "score": 1.0 }, "proximity": { "order": 2, "score": 1.0 }, "attribute": { "order": 3, "attributes_ranking_order": 1.0, "attributes_query_word_order": 0.8181818181818182, "score": 0.8181818181818182 }, "exactness": { "order": 4, "matchType": "noExactMatch", "score": 0.3333333333333333 } } } ], "query": "Badman dark knight returns", "processingTimeMs": 9, "limit": 5, "offset": 0, "estimatedTotalHits": 46 } ``` </details> - If adding a `showRankingScoreDetails` parameter to the search query, then the returned documents will now contain an additional `_rankingScoreDetails` field that is a JSON object containing one field per ranking rule that was applied, whose value is a JSON object with the following fields: - `order`: a number indicating the order this rule was applied (0 is the first applied ranking rule) - `score` (except for `sort` and `geosort`): a float indicating how the document matched this particular rule. - other fields that are specific to the rule, indicating for example how many words matched for a document and how many typos were counted in a matching document. - If the `displayableAttributes` list is defined in the settings of the index, any ranking rule using an attribute **not** part of that list will be marked as `<hidden-rule>` in the `_rankingScoreDetails`. - Search queries that are part of a `multi-search` requests are modified in the same way and each of the queries can take the `showRankingScore` and `showRankingScoreDetails` parameters independently. The results are still returned in separate lists and providing a unified list of results between multiple queries is not in the scope of this PR (but is unblocked by this PR and can be done manually by using the scores of the various documents). ### Implementation standpoint - Fix difference in how the position of terms were computed at indexing time and query time: this difference meant that a query containing a hard separator would fail the exactness check. - Fix the id reported by the sort ranking rule (very minor) - Change how the cost of removing words is computed. After this change the cost no longer works for any other ranking rule than `words`. Also made `words` have a cost of 0 such that the entire cost of `words` is given by the termRemovalStrategy. The new cost computation makes it so the score is computed in a way consistent with the number of words in the query. Additionally, the words that appear in phrases in the query are also counted as matching words. - When any score computation is requested through `showRankingScore` or `showRankingScoreDetails`, remove optimization where ranking rules are not executed on buckets of a single document: this is important to allow the computation of an accurate score. - add virtual conditions to fid and position to always have the max cost: this ensures that the score is independent from the dataset - the Position ranking rule now takes into account the distance to the position of the word in the query instead of the distance to the position 0. - modified proximity ranking rule cost calculation so that the cost is 0 for documents that are perfectly matching the query - Add a new `milli::score_details` module containing all the types that are involved in score computation. - Make it so a bucket of result now contains a `ScoreDetails` and changed the ranking rules to produce their `ScoreDetails`. - Expose the scores in the REST API. - Add very light analytics for scoring. - Update the search tests to add the expected scores. Co-authored-by: Louis Dureuil <louis@meilisearch.com> |
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| README.md | ||
a concurrent indexer combined with fast and relevant search algorithms
Introduction
This crate contains the internal engine used by Meilisearch.
It contains a library that can manage one and only one index. Meilisearch manages the multi-index itself. Milli is unable to store updates in a store: it is the job of something else above and this is why it is only able to process one update at a time.