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doc: Add a reading on the default typos and ranking rules
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It uses [RocksDB](https://github.com/facebook/rocksdb) as the internal key-value store. The key-value store allows us to handle updates and queries with small memory and CPU overheads.
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It uses [RocksDB](https://github.com/facebook/rocksdb) as the internal key-value store. The key-value store allows us to handle updates and queries with small memory and CPU overheads.
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You can [read the deep dive](deep-dive.md) if you want more information on the engine, it describes the whole process of generating updates and handling queries.
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You can [read the deep dive](deep-dive.md) if you want more information on the engine, it describes the whole process of generating updates and handling queries or you can take a look at the [typos and ranking rules](typos-ranking-rules.md) if you want to know the default rules used to sort the documents.
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We will be proud if you submit issues and pull requests. You can help to grow this project and start contributing by checking [issues tagged "good-first-issue"](https://github.com/Kerollmops/MeiliDB/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22). It is a good start!
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We will be proud if you submit issues and pull requests. You can help to grow this project and start contributing by checking [issues tagged "good-first-issue"](https://github.com/Kerollmops/MeiliDB/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22). It is a good start!
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typos-ranking-rules.md
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typos-ranking-rules.md
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# Typo and Ranking rules
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This is an explanation of the default rules used in MeiliDB.
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First we have to explain some terms that are used in this reading.
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- A query string is the full list of all the words that the end user is searching for results.
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- A query word is one of the words that compose the query string.
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## Typo rules
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The typo rules are used before sorting the documents. They are used to aggregate them, to choose which documents contain words similar to the queried words.
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We use a prefix _Levenshtein_ algorithm to check if the words match. The only difference with a Levenshtein algorithm is that it accepts every word that **starts with the query words** too. Therefore words are accepted if they start with or have the equal length.
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The Levenshtein distance between two words _M_ and _P_ is called "the minimum cost of transforming _M_ into _P_" by performing the following elementary operations:
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- substitution of a character of _M_ by a character other than _P_. (e.g. **k**itten → **s**itten)
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- insertion in _M_ of a character of _P_. (e.g. sittin → sittin**g**)
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- deleting a character from _M_. (e.g. satu**r**day → satuday)
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There are some rules about what can be considered "similar". These rules are **by word** and not for the whole query string.
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- If the query word is between 1 and 4 characters long therefore **no** typo is allowed, only documents that contains words that start or are exactly equal to this query word are considered valid for this request.
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- If the query word is between 5 and 8 characters long, **one** typo is allowed. Documents that contains words that match with one typo are retained for the next steps.
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- If the query word contains more than 8 characters, we accept a maximum of **two** typos.
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This means that "satuday", which is 7 characters long, use the second rule and every document containing words that have only **one** typo will match. For example:
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- "satuday" is accepted because it is exactly the same word.
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- "sat" is not accepted because the query word is not a prefix of it but the opposite.
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- "satu**r**day" is accepted because it contains **one** typo.
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- "s**u**tu**r**day" is not accepted because it contains **two** typos.
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## Ranking rules
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All documents that have been aggregated using the typo rules above can now be sorted. MeiliDB uses a bucket sort.
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What is a bucket sort? We sort all the documents with the first rule, for all documents that can't be separated we create a group and sort it using the second rule, and so on.
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Here is the list of all the default rules that are executed in this specific order by default:
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- _Number of Typos_ - The less typos there are beween the query words and the document words, the better is the document.
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- _Number of Words_ - A document containing more of the query words will be more important than one that contains less.
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- _Words Proximity_ - The closer the query words are in the document the better is the document.
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- _Attribute_ - A document containing the query words in a more important attribute than another document is considered better.
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- _Position_ - A document containing the query words at the start of an attribute is considered better than a document that contains them at the end.
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- _Exact_ - A document containing the query words in their exact form, not only a prefix of them, is considered better.
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