mirror of
https://github.com/meilisearch/meilisearch.git
synced 2025-11-04 01:46:28 +00:00
386 lines
14 KiB
Rust
386 lines
14 KiB
Rust
use std::cmp::Reverse;
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use std::collections::HashSet;
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use std::io::Write;
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use big_s::S;
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use bumpalo::Bump;
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use either::{Either, Left, Right};
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use heed::EnvOpenOptions;
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use maplit::{btreemap, hashset};
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use milli::progress::Progress;
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use milli::update::new::indexer;
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use milli::update::{IndexerConfig, Settings};
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use milli::vector::EmbeddingConfigs;
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use milli::{
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AscDesc, Criterion, DocumentId, FilterableAttributesRule, Index, Member, TermsMatchingStrategy,
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};
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use serde::{Deserialize, Deserializer};
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use slice_group_by::GroupBy;
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mod distinct;
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mod facet_distribution;
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mod filters;
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mod phrase_search;
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mod query_criteria;
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mod sort;
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mod typo_tolerance;
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pub const TEST_QUERY: &str = "hello world america";
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pub const EXTERNAL_DOCUMENTS_IDS: &[&str; 17] =
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&["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q"];
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pub const CONTENT: &str = include_str!("../assets/test_set.ndjson");
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pub fn setup_search_index_with_criteria(criteria: &[Criterion]) -> Index {
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let path = tempfile::tempdir().unwrap();
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let options = EnvOpenOptions::new();
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let mut options = options.read_txn_without_tls();
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options.map_size(10 * 1024 * 1024); // 10 MB
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let index = Index::new(options, &path, true).unwrap();
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let mut wtxn = index.write_txn().unwrap();
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let config = IndexerConfig::default();
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let mut builder = Settings::new(&mut wtxn, &index, &config);
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builder.set_criteria(criteria.to_vec());
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builder.set_filterable_fields(vec![
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FilterableAttributesRule::Field(S("tag")),
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FilterableAttributesRule::Field(S("asc_desc_rank")),
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FilterableAttributesRule::Field(S("_geo")),
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FilterableAttributesRule::Field(S("opt1")),
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FilterableAttributesRule::Field(S("opt1.opt2")),
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FilterableAttributesRule::Field(S("tag_in")),
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]);
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builder.set_sortable_fields(hashset! {
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S("tag"),
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S("asc_desc_rank"),
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});
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builder.set_synonyms(btreemap! {
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S("hello") => vec![S("good morning")],
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S("world") => vec![S("earth")],
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S("america") => vec![S("the united states")],
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});
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builder.set_searchable_fields(vec![S("title"), S("description")]);
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builder.execute(|_| (), || false).unwrap();
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wtxn.commit().unwrap();
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// index documents
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let config = IndexerConfig { max_memory: Some(10 * 1024 * 1024), ..Default::default() };
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let rtxn = index.read_txn().unwrap();
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let mut wtxn = index.write_txn().unwrap();
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let db_fields_ids_map = index.fields_ids_map(&rtxn).unwrap();
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let mut new_fields_ids_map = db_fields_ids_map.clone();
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let embedders = EmbeddingConfigs::default();
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let mut indexer = indexer::DocumentOperation::new();
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let mut file = tempfile::tempfile().unwrap();
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file.write_all(CONTENT.as_bytes()).unwrap();
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file.sync_all().unwrap();
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let payload = unsafe { memmap2::Mmap::map(&file).unwrap() };
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// index documents
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indexer.replace_documents(&payload).unwrap();
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let indexer_alloc = Bump::new();
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let (document_changes, operation_stats, primary_key) = indexer
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.into_changes(
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&indexer_alloc,
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&index,
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&rtxn,
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None,
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&mut new_fields_ids_map,
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&|| false,
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Progress::default(),
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)
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.unwrap();
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if let Some(error) = operation_stats.into_iter().find_map(|stat| stat.error) {
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panic!("{error}");
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}
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indexer::index(
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&mut wtxn,
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&index,
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&milli::ThreadPoolNoAbortBuilder::new().build().unwrap(),
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config.grenad_parameters(),
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&db_fields_ids_map,
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new_fields_ids_map,
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primary_key,
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&document_changes,
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embedders,
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&|| false,
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&Progress::default(),
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)
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.unwrap();
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wtxn.commit().unwrap();
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drop(rtxn);
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index
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}
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pub fn internal_to_external_ids(index: &Index, internal_ids: &[DocumentId]) -> Vec<String> {
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let rtxn = index.read_txn().unwrap();
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let docid_map = index.external_documents_ids();
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let docid_map: std::collections::HashMap<_, _> = EXTERNAL_DOCUMENTS_IDS
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.iter()
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.map(|id| (docid_map.get(&rtxn, id).unwrap().unwrap(), id))
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.collect();
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internal_ids.iter().map(|id| docid_map.get(id).unwrap().to_string()).collect()
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}
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pub fn expected_order(
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criteria: &[Criterion],
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optional_words: TermsMatchingStrategy,
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sort_by: &[AscDesc],
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) -> Vec<TestDocument> {
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let dataset =
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serde_json::Deserializer::from_str(CONTENT).into_iter().map(|r| r.unwrap()).collect();
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let mut groups: Vec<Vec<TestDocument>> = vec![dataset];
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for criterion in criteria {
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let mut new_groups = Vec::new();
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for group in groups.iter_mut() {
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match criterion {
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Criterion::Attribute => {
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group.sort_by_key(|d| d.attribute_rank);
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new_groups
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.extend(group.linear_group_by_key(|d| d.attribute_rank).map(Vec::from));
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}
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Criterion::Exactness => {
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group.sort_by_key(|d| d.exact_rank);
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new_groups.extend(group.linear_group_by_key(|d| d.exact_rank).map(Vec::from));
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}
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Criterion::Proximity => {
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group.sort_by_key(|d| d.proximity_rank);
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new_groups
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.extend(group.linear_group_by_key(|d| d.proximity_rank).map(Vec::from));
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}
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Criterion::Sort if sort_by == [AscDesc::Asc(Member::Field(S("tag")))] => {
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group.sort_by_key(|d| d.sort_by_rank);
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new_groups.extend(group.linear_group_by_key(|d| d.sort_by_rank).map(Vec::from));
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}
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Criterion::Sort if sort_by == [AscDesc::Desc(Member::Field(S("tag")))] => {
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group.sort_by_key(|d| Reverse(d.sort_by_rank));
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new_groups.extend(group.linear_group_by_key(|d| d.sort_by_rank).map(Vec::from));
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}
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Criterion::Typo => {
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group.sort_by_key(|d| d.typo_rank);
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new_groups.extend(group.linear_group_by_key(|d| d.typo_rank).map(Vec::from));
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}
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Criterion::Words => {
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group.sort_by_key(|d| d.word_rank);
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new_groups.extend(group.linear_group_by_key(|d| d.word_rank).map(Vec::from));
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}
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Criterion::Asc(field_name) if field_name == "asc_desc_rank" => {
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group.sort_by_key(|d| d.asc_desc_rank);
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new_groups
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.extend(group.linear_group_by_key(|d| d.asc_desc_rank).map(Vec::from));
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}
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Criterion::Desc(field_name) if field_name == "asc_desc_rank" => {
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group.sort_by_key(|d| Reverse(d.asc_desc_rank));
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new_groups
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.extend(group.linear_group_by_key(|d| d.asc_desc_rank).map(Vec::from));
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}
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Criterion::Asc(_) | Criterion::Desc(_) | Criterion::Sort => {
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new_groups.push(group.clone())
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}
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}
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}
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groups = std::mem::take(&mut new_groups);
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}
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match optional_words {
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TermsMatchingStrategy::Last => groups.into_iter().flatten().collect(),
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TermsMatchingStrategy::Frequency => groups.into_iter().flatten().collect(),
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TermsMatchingStrategy::All => {
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groups.into_iter().flatten().filter(|d| d.word_rank == 0).collect()
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}
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}
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}
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fn execute_filter(filter: &str, document: &TestDocument) -> Option<String> {
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let mut id = None;
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if let Some((field, filter)) = filter.split_once("!=") {
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if field == "tag" && document.tag != filter
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|| (field == "asc_desc_rank"
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&& Ok(&document.asc_desc_rank) != filter.parse::<u32>().as_ref())
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{
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id = Some(document.id.clone())
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}
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} else if let Some((field, filter)) = filter.split_once('=') {
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if field == "tag" && document.tag == filter
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|| (field == "asc_desc_rank"
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&& document.asc_desc_rank == filter.parse::<u32>().unwrap())
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{
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id = Some(document.id.clone())
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}
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} else if let Some(("asc_desc_rank", filter)) = filter.split_once('<') {
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if document.asc_desc_rank < filter.parse().unwrap() {
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id = Some(document.id.clone())
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}
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} else if let Some(("asc_desc_rank", filter)) = filter.split_once('>') {
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if document.asc_desc_rank > filter.parse().unwrap() {
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id = Some(document.id.clone())
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}
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} else if filter.starts_with("_geoRadius") {
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id = (document.geo_rank < 100000).then(|| document.id.clone());
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} else if filter.starts_with("NOT _geoRadius") {
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id = (document.geo_rank > 1000000).then(|| document.id.clone());
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} else if matches!(filter, "opt1 EXISTS" | "NOT opt1 NOT EXISTS") {
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id = document.opt1.is_some().then(|| document.id.clone());
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} else if matches!(filter, "NOT opt1 EXISTS" | "opt1 NOT EXISTS") {
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id = document.opt1.is_none().then(|| document.id.clone());
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} else if matches!(filter, "opt1.opt2 EXISTS") {
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if document.opt1opt2.is_some() {
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id = Some(document.id.clone());
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} else if let Some(opt1) = &document.opt1 {
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id = contains_key_rec(opt1, "opt2").then(|| document.id.clone());
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}
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} else if matches!(filter, "opt1 IS NULL" | "NOT opt1 IS NOT NULL") {
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id = document.opt1.as_ref().is_some_and(|v| v.is_null()).then(|| document.id.clone());
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} else if matches!(filter, "NOT opt1 IS NULL" | "opt1 IS NOT NULL") {
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id = document.opt1.as_ref().is_none_or(|v| !v.is_null()).then(|| document.id.clone());
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} else if matches!(filter, "opt1.opt2 IS NULL") {
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if document.opt1opt2.as_ref().is_some_and(|v| v.is_null()) {
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id = Some(document.id.clone());
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} else if let Some(opt1) = &document.opt1 {
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if !opt1.is_null() {
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id = contains_null_rec(opt1, "opt2").then(|| document.id.clone());
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}
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}
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} else if matches!(filter, "opt1 IS EMPTY" | "NOT opt1 IS NOT EMPTY") {
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id = document.opt1.as_ref().is_some_and(is_empty_value).then(|| document.id.clone());
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} else if matches!(filter, "NOT opt1 IS EMPTY" | "opt1 IS NOT EMPTY") {
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id = document.opt1.as_ref().is_none_or(|v| !is_empty_value(v)).then(|| document.id.clone());
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} else if matches!(filter, "opt1.opt2 IS EMPTY") {
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if document.opt1opt2.as_ref().is_some_and(is_empty_value) {
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id = Some(document.id.clone());
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}
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} else if matches!(
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filter,
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"tag_in IN[1, 2, 3, four, five]" | "NOT tag_in NOT IN[1, 2, 3, four, five]"
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) {
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id = matches!(document.id.as_str(), "A" | "B" | "C" | "D" | "E")
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.then(|| document.id.clone());
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} else if matches!(filter, "tag_in NOT IN[1, 2, 3, four, five]") {
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id = (!matches!(document.id.as_str(), "A" | "B" | "C" | "D" | "E"))
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.then(|| document.id.clone());
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}
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id
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}
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pub fn is_empty_value(v: &serde_json::Value) -> bool {
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match v {
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serde_json::Value::String(s) => s.is_empty(),
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serde_json::Value::Array(a) => a.is_empty(),
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serde_json::Value::Object(o) => o.is_empty(),
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_ => false,
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}
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}
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pub fn contains_key_rec(v: &serde_json::Value, key: &str) -> bool {
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match v {
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serde_json::Value::Array(v) => {
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for v in v.iter() {
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if contains_key_rec(v, key) {
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return true;
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}
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}
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false
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}
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serde_json::Value::Object(v) => {
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for (k, v) in v.iter() {
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if k == key || contains_key_rec(v, key) {
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return true;
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}
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}
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false
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}
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_ => false,
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}
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}
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pub fn contains_null_rec(v: &serde_json::Value, key: &str) -> bool {
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match v {
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serde_json::Value::Object(v) => {
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for (k, v) in v.iter() {
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if k == key && v.is_null() || contains_null_rec(v, key) {
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return true;
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}
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}
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false
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}
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serde_json::Value::Array(v) => {
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for v in v.iter() {
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if contains_null_rec(v, key) {
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return true;
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}
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}
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false
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}
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_ => false,
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}
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}
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pub fn expected_filtered_ids(filters: Vec<Either<Vec<&str>, &str>>) -> HashSet<String> {
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let dataset: Vec<TestDocument> =
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serde_json::Deserializer::from_str(CONTENT).into_iter().map(|r| r.unwrap()).collect();
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let mut filtered_ids: HashSet<_> = dataset.iter().map(|d| d.id.clone()).collect();
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for either in filters {
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let ids = match either {
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Left(array) => array
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.into_iter()
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.map(|f| {
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let ids: HashSet<String> =
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dataset.iter().filter_map(|d| execute_filter(f, d)).collect();
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ids
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})
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.reduce(|a, b| a.union(&b).cloned().collect())
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.unwrap(),
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Right(filter) => {
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let ids: HashSet<String> =
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dataset.iter().filter_map(|d| execute_filter(filter, d)).collect();
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ids
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}
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};
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filtered_ids = filtered_ids.intersection(&ids).cloned().collect();
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}
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filtered_ids
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}
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#[derive(Debug, Clone, Deserialize, PartialEq, Eq)]
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pub struct TestDocument {
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pub id: String,
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pub word_rank: u32,
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pub typo_rank: u32,
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pub proximity_rank: u32,
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pub attribute_rank: u32,
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pub exact_rank: u32,
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pub asc_desc_rank: u32,
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pub sort_by_rank: u32,
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pub geo_rank: u32,
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pub title: String,
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pub description: String,
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pub tag: String,
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#[serde(default, deserialize_with = "some_option")]
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pub opt1: Option<serde_json::Value>,
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#[serde(default, deserialize_with = "some_option", rename = "opt1.opt2")]
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pub opt1opt2: Option<serde_json::Value>,
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}
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fn some_option<'de, D>(deserializer: D) -> Result<Option<serde_json::Value>, D::Error>
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where
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D: Deserializer<'de>,
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{
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let result = serde_json::Value::deserialize(deserializer)?;
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Ok(Some(result))
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}
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